Hotel Data - Analyze over time
Reshu Gupta
2024-04-29
Initial setup and Configure the data set. Load the data file in
variable hotel_data.
Data set - Hotels : This data comes from an
open hotel booking demand data-set of hotels like City Hotel, Resort
Hotel.
Load dataset
hotel_data <- read.csv(file.choose())Ask: Select a column of your data that encodes time (e.g., “date”, “timestamp”, “year”, etc.). Convert this into a Date in R.
#head(hotel_data)
head(hotel_data[,c("hotel","lead_time","market_segment","distribution_channel","country","reservation_status_date")],5)## hotel lead_time market_segment distribution_channel country
## 1 Resort Hotel 342 Direct Direct PRT
## 2 Resort Hotel 737 Direct Direct PRT
## 3 Resort Hotel 7 Direct Direct GBR
## 4 Resort Hotel 13 Corporate Corporate GBR
## 5 Resort Hotel 14 Online TA TA/TO GBR
## reservation_status_date
## 1 2015-07-01
## 2 2015-07-01
## 3 2015-07-02
## 4 2015-07-02
## 5 2015-07-03## hotel is_canceled lead_time arrival_date_year arrival_date_month
## 1 Resort Hotel 0 342 2015 July
## 2 Resort Hotel 0 737 2015 July
## 3 Resort Hotel 0 7 2015 July
## 4 Resort Hotel 0 13 2015 July
## 5 Resort Hotel 0 14 2015 July
## 6 Resort Hotel 0 14 2015 July
## arrival_date_week_number arrival_date_day_of_month stays_in_weekend_nights
## 1 27 1 0
## 2 27 1 0
## 3 27 1 0
## 4 27 1 0
## 5 27 1 0
## 6 27 1 0
## stays_in_week_nights adults children babies meal country market_segment
## 1 0 2 0 0 BB PRT Direct
## 2 0 2 0 0 BB PRT Direct
## 3 1 1 0 0 BB GBR Direct
## 4 1 1 0 0 BB GBR Corporate
## 5 2 2 0 0 BB GBR Online TA
## 6 2 2 0 0 BB GBR Online TA
## distribution_channel is_repeated_guest previous_cancellations
## 1 Direct 0 0
## 2 Direct 0 0
## 3 Direct 0 0
## 4 Corporate 0 0
## 5 TA/TO 0 0
## 6 TA/TO 0 0
## previous_bookings_not_canceled reserved_room_type assigned_room_type
## 1 0 C C
## 2 0 C C
## 3 0 A C
## 4 0 A A
## 5 0 A A
## 6 0 A A
## booking_changes deposit_type agent company days_in_waiting_list customer_type
## 1 3 No Deposit NULL NULL 0 Transient
## 2 4 No Deposit NULL NULL 0 Transient
## 3 0 No Deposit NULL NULL 0 Transient
## 4 0 No Deposit 304 NULL 0 Transient
## 5 0 No Deposit 240 NULL 0 Transient
## 6 0 No Deposit 240 NULL 0 Transient
## adr required_car_parking_spaces total_of_special_requests reservation_status
## 1 0 0 0 Check-Out
## 2 0 0 0 Check-Out
## 3 75 0 0 Check-Out
## 4 75 0 0 Check-Out
## 5 98 0 1 Check-Out
## 6 98 0 1 Check-Out
## reservation_status_date time
## 1 2015-07-01 22:07
## 2 2015-07-01 16:22
## 3 2015-07-02 01:19
## 4 2015-07-02 07:48
## 5 2015-07-03 00:42
## 6 2015-07-03 05:04Explanation :
Column “reservation_status_date” does have
date data in my dataset which can be used here.
reservation_status_date : Date at which the last status was set. This
variable can be used in conjunction with the Reservation Status to
understand when was the booking canceled or when did the customer
checked-out of the hotel. (Definition from Data source: https://github.com/rfordatascience/tidytuesday/tree/master/data/2020/2020-02-11
)
head(hotel_data)## hotel is_canceled lead_time arrival_date_year arrival_date_month
## 1 Resort Hotel 0 342 2015 July
## 2 Resort Hotel 0 737 2015 July
## 3 Resort Hotel 0 7 2015 July
## 4 Resort Hotel 0 13 2015 July
## 5 Resort Hotel 0 14 2015 July
## 6 Resort Hotel 0 14 2015 July
## arrival_date_week_number arrival_date_day_of_month stays_in_weekend_nights
## 1 27 1 0
## 2 27 1 0
## 3 27 1 0
## 4 27 1 0
## 5 27 1 0
## 6 27 1 0
## stays_in_week_nights adults children babies meal country market_segment
## 1 0 2 0 0 BB PRT Direct
## 2 0 2 0 0 BB PRT Direct
## 3 1 1 0 0 BB GBR Direct
## 4 1 1 0 0 BB GBR Corporate
## 5 2 2 0 0 BB GBR Online TA
## 6 2 2 0 0 BB GBR Online TA
## distribution_channel is_repeated_guest previous_cancellations
## 1 Direct 0 0
## 2 Direct 0 0
## 3 Direct 0 0
## 4 Corporate 0 0
## 5 TA/TO 0 0
## 6 TA/TO 0 0
## previous_bookings_not_canceled reserved_room_type assigned_room_type
## 1 0 C C
## 2 0 C C
## 3 0 A C
## 4 0 A A
## 5 0 A A
## 6 0 A A
## booking_changes deposit_type agent company days_in_waiting_list customer_type
## 1 3 No Deposit NULL NULL 0 Transient
## 2 4 No Deposit NULL NULL 0 Transient
## 3 0 No Deposit NULL NULL 0 Transient
## 4 0 No Deposit 304 NULL 0 Transient
## 5 0 No Deposit 240 NULL 0 Transient
## 6 0 No Deposit 240 NULL 0 Transient
## adr required_car_parking_spaces total_of_special_requests reservation_status
## 1 0 0 0 Check-Out
## 2 0 0 0 Check-Out
## 3 75 0 0 Check-Out
## 4 75 0 0 Check-Out
## 5 98 0 1 Check-Out
## 6 98 0 1 Check-Out
## reservation_status_date time
## 1 2015-07-01 13:27
## 2 2015-07-01 17:46
## 3 2015-07-02 04:24
## 4 2015-07-02 20:23
## 5 2015-07-03 03:48
## 6 2015-07-03 06:21# Convert reservation_status_date to Date format
hotel_data$date <- as.Date(hotel_data$reservation_status_date,"%Y-%m-%d")
#Print top 5 row of date
head(hotel_data$date)## [1] "2015-07-01" "2015-07-01" "2015-07-02" "2015-07-02" "2015-07-03"
## [6] "2015-07-03"#find duplicate row
duplicates <- hotel_data %>%
group_by(date, response_variable = is_canceled) %>%
summarize(n = n()) %>%
filter(n > 1)## `summarise()` has grouped output by 'date'. You can override using the
## `.groups` argument.#Remove duplicate row from dataset
hotel_data <- distinct(hotel_data)
duplicate_rows <- hotel_data[duplicated(hotel_data[, c("date", "is_canceled")]), ]
hotel_data <- hotel_data[!duplicated(hotel_data[, c("date", "is_canceled")]), ]
hotel_data <- hotel_data %>% distinct(date, .keep_all = TRUE)
# Create a tsibble object with date and response variable
ts_data <- hotel_data %>%
select(date , response_variable = is_canceled) %>%
as_tsibble(index = date)
# Plot the data over time
ggplot(ts_data, aes(x = date, y = response_variable)) +
geom_line() +
labs(title = "Response Variable- Cancelation Over Time", x = "Date", y = "Response Variable - 'is_canceled'") +
theme_minimal()
Above graph show the
relationship between reservation date and cancellation. which is week in
nature.
# Fit linear regression model
lm_model <- lm(response_variable ~ date, data = ts_data)
# Check for trend significance
trend_significance <- summary(lm_model)$coefficients
# Print the summary of linear regression model
summary(lm_model)##
## Call:
## lm(formula = response_variable ~ date, data = ts_data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.7818 -0.4712 0.2439 0.3706 0.7815
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -8.4510055 0.9836631 -8.591 <2e-16 ***
## date 0.0005299 0.0000580 9.136 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.4782 on 924 degrees of freedom
## Multiple R-squared: 0.08285, Adjusted R-squared: 0.08185
## F-statistic: 83.47 on 1 and 924 DF, p-value: < 2.2e-16# Print the trend significance test results
print(trend_significance)## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -8.4510054898 9.836631e-01 -8.591362 3.620816e-17
## date 0.0005299228 5.800405e-05 9.135963 4.007237e-19Explanation :
Above linear regression model does have information about the relationship between reservation date and response_variable(is_canceled).However,in above analysis, R-squared value(0.08285) is low means there is week relationship between them.
Coefficients:Estimate value -8.4510054898 showes the estimated coefficients for the intercept and resevation date.
Std. Error : 9.836631e-01 standard errors of the estimate.
t value : -8.591362 t-statistic for testing the null hypothesis when coefficient is zero.
p-value : 3.620816e-17 associated with the t-statistic and indicating the significance of coefficient.
Moreover,Above result indicating that there is a relationship between
the resevation date and the response_variable i.e is_canceled. The
coefficient suggests that for each date plus, there is an increase of
0.0005299 in the response variable although,it is very small.It is not
too significant.
In real world, reservation date and Cancellation
does not have significant relationship, that means, this can not predict
that booking can be canceled for particular date but this analysis may
bring the behaviors of cancellation if any.
Do you need to subset the data for multiple trends?
Yes, There could be muliple subset of Hotel data set for example, Hotel wise sub data, market segment wise subset etc.
# Unique market segments and countries data in the dataset
segments <- unique(hotel_data$market_segment)
countries <- unique(hotel_data$country)
# List to store regression results for each subset
regression_results <- list()
# Loop through each combination of market segment and country
for (segment in segments) {
for (country in countries) {
# Subset the data for the current combination
subset_data <- hotel_data[hotel_data$market_segment == segment & hotel_data$country == country, ]
# Check if the subset contains data
if (nrow(subset_data) > 0) {
dependent_variable <- "is_canceled"
# Perform linear regression
lm_model <- lm(formula = paste(dependent_variable, "~ date"), data = subset_data)
# Store regression results
regression_results[[paste(segment, country, sep = "_")]] <- summary(lm_model)
print("**************************************************")
print(paste(" >>>> Market Segment:", segment, "Country:", country))
print(summary(lm_model))
print("**************************************************")
} else {
# Print a message if the subset is empty
print(paste("No data for Market Segment:", segment, "Country:", country))
}
}
}## [1] "**************************************************"
## [1] " >>>> Market Segment: Direct Country: PRT"
##
## Call:
## lm(formula = paste(dependent_variable, "~ date"), data = subset_data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.8959 0.1058 0.1507 0.2234 0.4376
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -4.8086334 2.3013171 -2.090 0.0394 *
## date 0.0003277 0.0001351 2.425 0.0172 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.4121 on 94 degrees of freedom
## Multiple R-squared: 0.05887, Adjusted R-squared: 0.04886
## F-statistic: 5.88 on 1 and 94 DF, p-value: 0.01722
##
## [1] "**************************************************"
## [1] "**************************************************"
## [1] " >>>> Market Segment: Direct Country: GBR"
##
## Call:
## lm(formula = paste(dependent_variable, "~ date"), data = subset_data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.62172 -0.16520 -0.03029 0.16415 0.70481
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -2.741e+01 6.841e+00 -4.007 0.00149 **
## date 1.641e-03 4.017e-04 4.085 0.00129 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.3546 on 13 degrees of freedom
## Multiple R-squared: 0.5621, Adjusted R-squared: 0.5284
## F-statistic: 16.69 on 1 and 13 DF, p-value: 0.001289
##
## [1] "**************************************************"
## [1] "**************************************************"
## [1] " >>>> Market Segment: Direct Country: NULL"
##
## Call:
## lm(formula = paste(dependent_variable, "~ date"), data = subset_data)
##
## Residuals:
## ALL 2 residuals are 0: no residual degrees of freedom!
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0 NaN NaN NaN
## date 0 NaN NaN NaN
##
## Residual standard error: NaN on 0 degrees of freedom
## Multiple R-squared: NaN, Adjusted R-squared: NaN
## F-statistic: NaN on 1 and 0 DF, p-value: NA
##
## [1] "**************************************************"
## [1] "**************************************************"
## [1] " >>>> Market Segment: Direct Country: IRL"
##
## Call:
## lm(formula = paste(dependent_variable, "~ date"), data = subset_data)
##
## Residuals:
## Min 1Q Median 3Q Max
## 0 0 0 0 0
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0 0 NaN NaN
## date 0 0 NaN NaN
##
## Residual standard error: 0 on 6 degrees of freedom
## Multiple R-squared: NaN, Adjusted R-squared: NaN
## F-statistic: NaN on 1 and 6 DF, p-value: NA
##
## [1] "**************************************************"
## [1] "**************************************************"
## [1] " >>>> Market Segment: Direct Country: USA"
##
## Call:
## lm(formula = paste(dependent_variable, "~ date"), data = subset_data)
##
## Residuals:
## 273 682 790 813
## -0.03517 0.15860 -0.03767 -0.08577
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -3.232e+01 5.187e+00 -6.231 0.0248 *
## date 1.924e-03 3.018e-04 6.376 0.0237 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.1326 on 2 degrees of freedom
## Multiple R-squared: 0.9531, Adjusted R-squared: 0.9297
## F-statistic: 40.65 on 1 and 2 DF, p-value: 0.02373
##
## [1] "**************************************************"
## [1] "**************************************************"
## [1] " >>>> Market Segment: Direct Country: FRA"
##
## Call:
## lm(formula = paste(dependent_variable, "~ date"), data = subset_data)
##
## Residuals:
## ALL 2 residuals are 0: no residual degrees of freedom!
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0 NaN NaN NaN
## date 0 NaN NaN NaN
##
## Residual standard error: NaN on 0 degrees of freedom
## Multiple R-squared: NaN, Adjusted R-squared: NaN
## F-statistic: NaN on 1 and 0 DF, p-value: NA
##
## [1] "**************************************************"
## [1] "**************************************************"
## [1] " >>>> Market Segment: Direct Country: ESP"
##
## Call:
## lm(formula = paste(dependent_variable, "~ date"), data = subset_data)
##
## Residuals:
## 74 333 367 568 586
## -0.2670 0.3051 0.2480 -0.1338 -0.1523
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -3.043e+01 1.276e+01 -2.385 0.0972 .
## date 1.844e-03 7.536e-04 2.447 0.0919 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.2983 on 3 degrees of freedom
## Multiple R-squared: 0.6663, Adjusted R-squared: 0.5551
## F-statistic: 5.99 on 1 and 3 DF, p-value: 0.09189
##
## [1] "**************************************************"
## [1] "No data for Market Segment: Direct Country: ITA"
## [1] "No data for Market Segment: Direct Country: DNK"
## [1] "No data for Market Segment: Direct Country: SWE"
## [1] "**************************************************"
## [1] " >>>> Market Segment: Direct Country: RUS"
##
## Call:
## lm(formula = paste(dependent_variable, "~ date"), data = subset_data)
##
## Residuals:
## ALL 1 residuals are 0: no residual degrees of freedom!
##
## Coefficients: (1 not defined because of singularities)
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0 NaN NaN NaN
## date NA NA NA NA
##
## Residual standard error: NaN on 0 degrees of freedom
##
## [1] "**************************************************"
## [1] "No data for Market Segment: Direct Country: MAR"
## [1] "No data for Market Segment: Direct Country: ROU"
## [1] "No data for Market Segment: Direct Country: NLD"
## [1] "No data for Market Segment: Direct Country: UKR"
## [1] "**************************************************"
## [1] " >>>> Market Segment: Direct Country: DEU"
##
## Call:
## lm(formula = paste(dependent_variable, "~ date"), data = subset_data)
##
## Residuals:
## ALL 1 residuals are 0: no residual degrees of freedom!
##
## Coefficients: (1 not defined because of singularities)
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0 NaN NaN NaN
## date NA NA NA NA
##
## Residual standard error: NaN on 0 degrees of freedom
##
## [1] "**************************************************"
## [1] "No data for Market Segment: Direct Country: POL"
## [1] "**************************************************"
## [1] " >>>> Market Segment: Direct Country: BEL"
##
## Call:
## lm(formula = paste(dependent_variable, "~ date"), data = subset_data)
##
## Residuals:
## ALL 1 residuals are 0: no residual degrees of freedom!
##
## Coefficients: (1 not defined because of singularities)
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0 NaN NaN NaN
## date NA NA NA NA
##
## Residual standard error: NaN on 0 degrees of freedom
##
## [1] "**************************************************"
## [1] "No data for Market Segment: Direct Country: FIN"
## [1] "No data for Market Segment: Direct Country: CHE"
## [1] "No data for Market Segment: Direct Country: AUT"
## [1] "No data for Market Segment: Direct Country: DZA"
## [1] "No data for Market Segment: Direct Country: ZAF"
## [1] "**************************************************"
## [1] " >>>> Market Segment: Direct Country: CN"
##
## Call:
## lm(formula = paste(dependent_variable, "~ date"), data = subset_data)
##
## Residuals:
## ALL 1 residuals are 0: no residual degrees of freedom!
##
## Coefficients: (1 not defined because of singularities)
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0 NaN NaN NaN
## date NA NA NA NA
##
## Residual standard error: NaN on 0 degrees of freedom
##
## [1] "**************************************************"
## [1] "No data for Market Segment: Direct Country: KOR"
## [1] "**************************************************"
## [1] " >>>> Market Segment: Direct Country: LUX"
##
## Call:
## lm(formula = paste(dependent_variable, "~ date"), data = subset_data)
##
## Residuals:
## ALL 1 residuals are 0: no residual degrees of freedom!
##
## Coefficients: (1 not defined because of singularities)
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0 NaN NaN NaN
## date NA NA NA NA
##
## Residual standard error: NaN on 0 degrees of freedom
##
## [1] "**************************************************"
## [1] "No data for Market Segment: Direct Country: ARE"
## [1] "**************************************************"
## [1] " >>>> Market Segment: Direct Country: BRA"
##
## Call:
## lm(formula = paste(dependent_variable, "~ date"), data = subset_data)
##
## Residuals:
## ALL 1 residuals are 0: no residual degrees of freedom!
##
## Coefficients: (1 not defined because of singularities)
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1 NaN NaN NaN
## date NA NA NA NA
##
## Residual standard error: NaN on 0 degrees of freedom
##
## [1] "**************************************************"
## [1] "No data for Market Segment: Direct Country: JAM"
## [1] "No data for Market Segment: Direct Country: AUS"
## [1] "**************************************************"
## [1] " >>>> Market Segment: Direct Country: NOR"
##
## Call:
## lm(formula = paste(dependent_variable, "~ date"), data = subset_data)
##
## Residuals:
## ALL 1 residuals are 0: no residual degrees of freedom!
##
## Coefficients: (1 not defined because of singularities)
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0 NaN NaN NaN
## date NA NA NA NA
##
## Residual standard error: NaN on 0 degrees of freedom
##
## [1] "**************************************************"
## [1] "No data for Market Segment: Direct Country: ISR"
## [1] "No data for Market Segment: Direct Country: CHN"
## [1] "No data for Market Segment: Direct Country: LTU"
## [1] "No data for Market Segment: Direct Country: EST"
## [1] "No data for Market Segment: Direct Country: GGY"
## [1] "No data for Market Segment: Direct Country: NGA"
## [1] "No data for Market Segment: Direct Country: COL"
## [1] "**************************************************"
## [1] " >>>> Market Segment: Direct Country: AGO"
##
## Call:
## lm(formula = paste(dependent_variable, "~ date"), data = subset_data)
##
## Residuals:
## ALL 1 residuals are 0: no residual degrees of freedom!
##
## Coefficients: (1 not defined because of singularities)
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1 NaN NaN NaN
## date NA NA NA NA
##
## Residual standard error: NaN on 0 degrees of freedom
##
## [1] "**************************************************"
## [1] "No data for Market Segment: Direct Country: PHL"
## [1] "No data for Market Segment: Direct Country: JPN"
## [1] "**************************************************"
## [1] " >>>> Market Segment: Online TA Country: PRT"
##
## Call:
## lm(formula = paste(dependent_variable, "~ date"), data = subset_data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.8795 0.1257 0.1379 0.1601 0.1658
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 5.446e-02 1.332e+00 0.041 0.967
## date 4.738e-05 7.891e-05 0.600 0.549
##
## Residual standard error: 0.3543 on 197 degrees of freedom
## Multiple R-squared: 0.001827, Adjusted R-squared: -0.00324
## F-statistic: 0.3606 on 1 and 197 DF, p-value: 0.5489
##
## [1] "**************************************************"
## [1] "**************************************************"
## [1] " >>>> Market Segment: Online TA Country: GBR"
##
## Call:
## lm(formula = paste(dependent_variable, "~ date"), data = subset_data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.47267 -0.15652 -0.02702 0.19009 0.75968
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -31.893893 2.205067 -14.46 <2e-16 ***
## date 0.001904 0.000130 14.65 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.2384 on 63 degrees of freedom
## Multiple R-squared: 0.7732, Adjusted R-squared: 0.7696
## F-statistic: 214.7 on 1 and 63 DF, p-value: < 2.2e-16
##
## [1] "**************************************************"
## [1] "No data for Market Segment: Online TA Country: NULL"
## [1] "**************************************************"
## [1] " >>>> Market Segment: Online TA Country: IRL"
##
## Call:
## lm(formula = paste(dependent_variable, "~ date"), data = subset_data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.70157 -0.18192 -0.03114 0.12925 0.63562
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -1.900e+01 4.100e+00 -4.636 5.71e-05 ***
## date 1.131e-03 2.422e-04 4.672 5.14e-05 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.2815 on 32 degrees of freedom
## Multiple R-squared: 0.4055, Adjusted R-squared: 0.3869
## F-statistic: 21.83 on 1 and 32 DF, p-value: 5.145e-05
##
## [1] "**************************************************"
## [1] "**************************************************"
## [1] " >>>> Market Segment: Online TA Country: USA"
##
## Call:
## lm(formula = paste(dependent_variable, "~ date"), data = subset_data)
##
## Residuals:
## 22 108 593 705 772 854
## -0.02513 -0.08446 0.20206 0.15311 -0.06197 -0.18361
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -2.463e+01 3.785e+00 -6.506 0.00288 **
## date 1.483e-03 2.219e-04 6.683 0.00261 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.1655 on 4 degrees of freedom
## Multiple R-squared: 0.9178, Adjusted R-squared: 0.8973
## F-statistic: 44.67 on 1 and 4 DF, p-value: 0.002606
##
## [1] "**************************************************"
## [1] "**************************************************"
## [1] " >>>> Market Segment: Online TA Country: FRA"
##
## Call:
## lm(formula = paste(dependent_variable, "~ date"), data = subset_data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.32792 -0.13891 -0.06698 0.10198 0.39800
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -3.139e+01 3.573e+00 -8.786 2.66e-07 ***
## date 1.877e-03 2.093e-04 8.968 2.05e-07 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.2017 on 15 degrees of freedom
## Multiple R-squared: 0.8428, Adjusted R-squared: 0.8323
## F-statistic: 80.43 on 1 and 15 DF, p-value: 2.052e-07
##
## [1] "**************************************************"
## [1] "**************************************************"
## [1] " >>>> Market Segment: Online TA Country: ESP"
##
## Call:
## lm(formula = paste(dependent_variable, "~ date"), data = subset_data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.38662 -0.18162 -0.00927 0.14267 0.48380
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -2.134e+01 2.677e+00 -7.970 6.16e-10 ***
## date 1.296e-03 1.563e-04 8.293 2.19e-10 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.2163 on 42 degrees of freedom
## Multiple R-squared: 0.6209, Adjusted R-squared: 0.6118
## F-statistic: 68.78 on 1 and 42 DF, p-value: 2.189e-10
##
## [1] "**************************************************"
## [1] "**************************************************"
## [1] " >>>> Market Segment: Online TA Country: ITA"
##
## Call:
## lm(formula = paste(dependent_variable, "~ date"), data = subset_data)
##
## Residuals:
## 372 481 688 861
## 0.06374 -0.21751 0.28969 -0.13592
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -4.216e+01 1.286e+01 -3.279 0.0818 .
## date 2.489e-03 7.502e-04 3.318 0.0801 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.2773 on 2 degrees of freedom
## Multiple R-squared: 0.8462, Adjusted R-squared: 0.7694
## F-statistic: 11.01 on 1 and 2 DF, p-value: 0.08009
##
## [1] "**************************************************"
## [1] "**************************************************"
## [1] " >>>> Market Segment: Online TA Country: DNK"
##
## Call:
## lm(formula = paste(dependent_variable, "~ date"), data = subset_data)
##
## Residuals:
## ALL 1 residuals are 0: no residual degrees of freedom!
##
## Coefficients: (1 not defined because of singularities)
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0 NaN NaN NaN
## date NA NA NA NA
##
## Residual standard error: NaN on 0 degrees of freedom
##
## [1] "**************************************************"
## [1] "**************************************************"
## [1] " >>>> Market Segment: Online TA Country: SWE"
##
## Call:
## lm(formula = paste(dependent_variable, "~ date"), data = subset_data)
##
## Residuals:
## ALL 2 residuals are 0: no residual degrees of freedom!
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -24.121739 NaN NaN NaN
## date 0.001449 NaN NaN NaN
##
## Residual standard error: NaN on 0 degrees of freedom
## Multiple R-squared: 1, Adjusted R-squared: NaN
## F-statistic: NaN on 1 and 0 DF, p-value: NA
##
## [1] "**************************************************"
## [1] "**************************************************"
## [1] " >>>> Market Segment: Online TA Country: RUS"
##
## Call:
## lm(formula = paste(dependent_variable, "~ date"), data = subset_data)
##
## Residuals:
## 111 188 829 836
## 0.041410 -0.045215 -0.002502 0.006307
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -2.451e+01 1.093e+00 -22.42 0.00198 **
## date 1.468e-03 6.417e-05 22.88 0.00190 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.04362 on 2 degrees of freedom
## Multiple R-squared: 0.9962, Adjusted R-squared: 0.9943
## F-statistic: 523.6 on 1 and 2 DF, p-value: 0.001904
##
## [1] "**************************************************"
## [1] "**************************************************"
## [1] " >>>> Market Segment: Online TA Country: MAR"
##
## Call:
## lm(formula = paste(dependent_variable, "~ date"), data = subset_data)
##
## Residuals:
## ALL 1 residuals are 0: no residual degrees of freedom!
##
## Coefficients: (1 not defined because of singularities)
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1 NaN NaN NaN
## date NA NA NA NA
##
## Residual standard error: NaN on 0 degrees of freedom
##
## [1] "**************************************************"
## [1] "**************************************************"
## [1] " >>>> Market Segment: Online TA Country: ROU"
##
## Call:
## lm(formula = paste(dependent_variable, "~ date"), data = subset_data)
##
## Residuals:
## 131 494 839
## -0.1586 0.3251 -0.1665
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -2.404e+01 1.380e+01 -1.741 0.332
## date 1.450e-03 8.102e-04 1.790 0.324
##
## Residual standard error: 0.3982 on 1 degrees of freedom
## Multiple R-squared: 0.7621, Adjusted R-squared: 0.5243
## F-statistic: 3.204 on 1 and 1 DF, p-value: 0.3243
##
## [1] "**************************************************"
## [1] "**************************************************"
## [1] " >>>> Market Segment: Online TA Country: NLD"
##
## Call:
## lm(formula = paste(dependent_variable, "~ date"), data = subset_data)
##
## Residuals:
## 136 164 300
## 0 0 0
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0 0 NaN NaN
## date 0 0 NaN NaN
##
## Residual standard error: 0 on 1 degrees of freedom
## Multiple R-squared: NaN, Adjusted R-squared: NaN
## F-statistic: NaN on 1 and 1 DF, p-value: NA
##
## [1] "**************************************************"
## [1] "**************************************************"
## [1] " >>>> Market Segment: Online TA Country: UKR"
##
## Call:
## lm(formula = paste(dependent_variable, "~ date"), data = subset_data)
##
## Residuals:
## ALL 1 residuals are 0: no residual degrees of freedom!
##
## Coefficients: (1 not defined because of singularities)
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0 NaN NaN NaN
## date NA NA NA NA
##
## Residual standard error: NaN on 0 degrees of freedom
##
## [1] "**************************************************"
## [1] "**************************************************"
## [1] " >>>> Market Segment: Online TA Country: DEU"
##
## Call:
## lm(formula = paste(dependent_variable, "~ date"), data = subset_data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.33748 -0.06092 0.03755 0.08814 0.26575
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -4.039e+01 6.054e+00 -6.673 0.000549 ***
## date 2.403e-03 3.557e-04 6.756 0.000513 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.1968 on 6 degrees of freedom
## Multiple R-squared: 0.8838, Adjusted R-squared: 0.8644
## F-statistic: 45.64 on 1 and 6 DF, p-value: 0.0005132
##
## [1] "**************************************************"
## [1] "**************************************************"
## [1] " >>>> Market Segment: Online TA Country: POL"
##
## Call:
## lm(formula = paste(dependent_variable, "~ date"), data = subset_data)
##
## Residuals:
## 184 421 660 763 776
## -0.25783 0.40968 0.05172 -0.09400 -0.10957
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -2.340e+01 9.636e+00 -2.428 0.0935 .
## date 1.415e-03 5.633e-04 2.512 0.0868 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.2932 on 3 degrees of freedom
## Multiple R-squared: 0.6777, Adjusted R-squared: 0.5703
## F-statistic: 6.308 on 1 and 3 DF, p-value: 0.08681
##
## [1] "**************************************************"
## [1] "**************************************************"
## [1] " >>>> Market Segment: Online TA Country: BEL"
##
## Call:
## lm(formula = paste(dependent_variable, "~ date"), data = subset_data)
##
## Residuals:
## ALL 2 residuals are 0: no residual degrees of freedom!
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0 NaN NaN NaN
## date 0 NaN NaN NaN
##
## Residual standard error: NaN on 0 degrees of freedom
## Multiple R-squared: NaN, Adjusted R-squared: NaN
## F-statistic: NaN on 1 and 0 DF, p-value: NA
##
## [1] "**************************************************"
## [1] "**************************************************"
## [1] " >>>> Market Segment: Online TA Country: FIN"
##
## Call:
## lm(formula = paste(dependent_variable, "~ date"), data = subset_data)
##
## Residuals:
## ALL 2 residuals are 0: no residual degrees of freedom!
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0 NaN NaN NaN
## date 0 NaN NaN NaN
##
## Residual standard error: NaN on 0 degrees of freedom
## Multiple R-squared: NaN, Adjusted R-squared: NaN
## F-statistic: NaN on 1 and 0 DF, p-value: NA
##
## [1] "**************************************************"
## [1] "**************************************************"
## [1] " >>>> Market Segment: Online TA Country: CHE"
##
## Call:
## lm(formula = paste(dependent_variable, "~ date"), data = subset_data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.30495 -0.19937 -0.01613 0.15483 0.41284
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -3.238e+01 8.607e+00 -3.762 0.00938 **
## date 1.920e-03 5.064e-04 3.791 0.00906 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.2713 on 6 degrees of freedom
## Multiple R-squared: 0.7055, Adjusted R-squared: 0.6564
## F-statistic: 14.37 on 1 and 6 DF, p-value: 0.009059
##
## [1] "**************************************************"
## [1] "No data for Market Segment: Online TA Country: AUT"
## [1] "No data for Market Segment: Online TA Country: DZA"
## [1] "**************************************************"
## [1] " >>>> Market Segment: Online TA Country: ZAF"
##
## Call:
## lm(formula = paste(dependent_variable, "~ date"), data = subset_data)
##
## Residuals:
## ALL 2 residuals are 0: no residual degrees of freedom!
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -72.017167 NaN NaN NaN
## date 0.004292 NaN NaN NaN
##
## Residual standard error: NaN on 0 degrees of freedom
## Multiple R-squared: 1, Adjusted R-squared: NaN
## F-statistic: NaN on 1 and 0 DF, p-value: NA
##
## [1] "**************************************************"
## [1] "**************************************************"
## [1] " >>>> Market Segment: Online TA Country: CN"
##
## Call:
## lm(formula = paste(dependent_variable, "~ date"), data = subset_data)
##
## Residuals:
## 253 571 738 739 787
## -0.10891 0.27523 -0.06778 0.02772 -0.12625
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -3.263e+01 7.662e+00 -4.258 0.0237 *
## date 1.949e-03 4.467e-04 4.363 0.0223 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.1905 on 3 degrees of freedom
## Multiple R-squared: 0.8639, Adjusted R-squared: 0.8185
## F-statistic: 19.04 on 1 and 3 DF, p-value: 0.02226
##
## [1] "**************************************************"
## [1] "**************************************************"
## [1] " >>>> Market Segment: Online TA Country: KOR"
##
## Call:
## lm(formula = paste(dependent_variable, "~ date"), data = subset_data)
##
## Residuals:
## ALL 1 residuals are 0: no residual degrees of freedom!
##
## Coefficients: (1 not defined because of singularities)
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0 NaN NaN NaN
## date NA NA NA NA
##
## Residual standard error: NaN on 0 degrees of freedom
##
## [1] "**************************************************"
## [1] "No data for Market Segment: Online TA Country: LUX"
## [1] "**************************************************"
## [1] " >>>> Market Segment: Online TA Country: ARE"
##
## Call:
## lm(formula = paste(dependent_variable, "~ date"), data = subset_data)
##
## Residuals:
## ALL 2 residuals are 0: no residual degrees of freedom!
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1.000e+00 NaN NaN NaN
## date 7.137e-19 NaN NaN NaN
##
## Residual standard error: NaN on 0 degrees of freedom
## Multiple R-squared: NaN, Adjusted R-squared: NaN
## F-statistic: NaN on 1 and 0 DF, p-value: NA
##
## [1] "**************************************************"
## [1] "**************************************************"
## [1] " >>>> Market Segment: Online TA Country: BRA"
##
## Call:
## lm(formula = paste(dependent_variable, "~ date"), data = subset_data)
##
## Residuals:
## ALL 2 residuals are 0: no residual degrees of freedom!
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -92.038043 NaN NaN NaN
## date 0.005435 NaN NaN NaN
##
## Residual standard error: NaN on 0 degrees of freedom
## Multiple R-squared: 1, Adjusted R-squared: NaN
## F-statistic: NaN on 1 and 0 DF, p-value: NA
##
## [1] "**************************************************"
## [1] "No data for Market Segment: Online TA Country: JAM"
## [1] "**************************************************"
## [1] " >>>> Market Segment: Online TA Country: AUS"
##
## Call:
## lm(formula = paste(dependent_variable, "~ date"), data = subset_data)
##
## Residuals:
## ALL 1 residuals are 0: no residual degrees of freedom!
##
## Coefficients: (1 not defined because of singularities)
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0 NaN NaN NaN
## date NA NA NA NA
##
## Residual standard error: NaN on 0 degrees of freedom
##
## [1] "**************************************************"
## [1] "**************************************************"
## [1] " >>>> Market Segment: Online TA Country: NOR"
##
## Call:
## lm(formula = paste(dependent_variable, "~ date"), data = subset_data)
##
## Residuals:
## ALL 2 residuals are 0: no residual degrees of freedom!
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -62.01095 NaN NaN NaN
## date 0.00365 NaN NaN NaN
##
## Residual standard error: NaN on 0 degrees of freedom
## Multiple R-squared: 1, Adjusted R-squared: NaN
## F-statistic: NaN on 1 and 0 DF, p-value: NA
##
## [1] "**************************************************"
## [1] "**************************************************"
## [1] " >>>> Market Segment: Online TA Country: ISR"
##
## Call:
## lm(formula = paste(dependent_variable, "~ date"), data = subset_data)
##
## Residuals:
## ALL 1 residuals are 0: no residual degrees of freedom!
##
## Coefficients: (1 not defined because of singularities)
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0 NaN NaN NaN
## date NA NA NA NA
##
## Residual standard error: NaN on 0 degrees of freedom
##
## [1] "**************************************************"
## [1] "**************************************************"
## [1] " >>>> Market Segment: Online TA Country: CHN"
##
## Call:
## lm(formula = paste(dependent_variable, "~ date"), data = subset_data)
##
## Residuals:
## ALL 1 residuals are 0: no residual degrees of freedom!
##
## Coefficients: (1 not defined because of singularities)
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1 NaN NaN NaN
## date NA NA NA NA
##
## Residual standard error: NaN on 0 degrees of freedom
##
## [1] "**************************************************"
## [1] "No data for Market Segment: Online TA Country: LTU"
## [1] "No data for Market Segment: Online TA Country: EST"
## [1] "**************************************************"
## [1] " >>>> Market Segment: Online TA Country: GGY"
##
## Call:
## lm(formula = paste(dependent_variable, "~ date"), data = subset_data)
##
## Residuals:
## ALL 1 residuals are 0: no residual degrees of freedom!
##
## Coefficients: (1 not defined because of singularities)
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1 NaN NaN NaN
## date NA NA NA NA
##
## Residual standard error: NaN on 0 degrees of freedom
##
## [1] "**************************************************"
## [1] "**************************************************"
## [1] " >>>> Market Segment: Online TA Country: NGA"
##
## Call:
## lm(formula = paste(dependent_variable, "~ date"), data = subset_data)
##
## Residuals:
## ALL 1 residuals are 0: no residual degrees of freedom!
##
## Coefficients: (1 not defined because of singularities)
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1 NaN NaN NaN
## date NA NA NA NA
##
## Residual standard error: NaN on 0 degrees of freedom
##
## [1] "**************************************************"
## [1] "**************************************************"
## [1] " >>>> Market Segment: Online TA Country: COL"
##
## Call:
## lm(formula = paste(dependent_variable, "~ date"), data = subset_data)
##
## Residuals:
## ALL 1 residuals are 0: no residual degrees of freedom!
##
## Coefficients: (1 not defined because of singularities)
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1 NaN NaN NaN
## date NA NA NA NA
##
## Residual standard error: NaN on 0 degrees of freedom
##
## [1] "**************************************************"
## [1] "**************************************************"
## [1] " >>>> Market Segment: Online TA Country: AGO"
##
## Call:
## lm(formula = paste(dependent_variable, "~ date"), data = subset_data)
##
## Residuals:
## ALL 1 residuals are 0: no residual degrees of freedom!
##
## Coefficients: (1 not defined because of singularities)
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1 NaN NaN NaN
## date NA NA NA NA
##
## Residual standard error: NaN on 0 degrees of freedom
##
## [1] "**************************************************"
## [1] "**************************************************"
## [1] " >>>> Market Segment: Online TA Country: PHL"
##
## Call:
## lm(formula = paste(dependent_variable, "~ date"), data = subset_data)
##
## Residuals:
## ALL 1 residuals are 0: no residual degrees of freedom!
##
## Coefficients: (1 not defined because of singularities)
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1 NaN NaN NaN
## date NA NA NA NA
##
## Residual standard error: NaN on 0 degrees of freedom
##
## [1] "**************************************************"
## [1] "**************************************************"
## [1] " >>>> Market Segment: Online TA Country: JPN"
##
## Call:
## lm(formula = paste(dependent_variable, "~ date"), data = subset_data)
##
## Residuals:
## ALL 1 residuals are 0: no residual degrees of freedom!
##
## Coefficients: (1 not defined because of singularities)
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0 NaN NaN NaN
## date NA NA NA NA
##
## Residual standard error: NaN on 0 degrees of freedom
##
## [1] "**************************************************"
## [1] "**************************************************"
## [1] " >>>> Market Segment: Offline TA/TO Country: PRT"
##
## Call:
## lm(formula = paste(dependent_variable, "~ date"), data = subset_data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.8702 0.1322 0.1390 0.1554 0.1587
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 3.592e-01 2.256e+00 0.159 0.874
## date 2.933e-05 1.335e-04 0.220 0.827
##
## Residual standard error: 0.3579 on 60 degrees of freedom
## Multiple R-squared: 0.0008044, Adjusted R-squared: -0.01585
## F-statistic: 0.0483 on 1 and 60 DF, p-value: 0.8268
##
## [1] "**************************************************"
## [1] "**************************************************"
## [1] " >>>> Market Segment: Offline TA/TO Country: GBR"
##
## Call:
## lm(formula = paste(dependent_variable, "~ date"), data = subset_data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.07581 -0.02021 -0.01127 0.01377 0.92894
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -2.221e+00 7.751e-01 -2.866 0.00490 **
## date 1.318e-04 4.583e-05 2.876 0.00475 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.08726 on 122 degrees of freedom
## Multiple R-squared: 0.06351, Adjusted R-squared: 0.05583
## F-statistic: 8.274 on 1 and 122 DF, p-value: 0.004749
##
## [1] "**************************************************"
## [1] "**************************************************"
## [1] " >>>> Market Segment: Offline TA/TO Country: NULL"
##
## Call:
## lm(formula = paste(dependent_variable, "~ date"), data = subset_data)
##
## Residuals:
## ALL 1 residuals are 0: no residual degrees of freedom!
##
## Coefficients: (1 not defined because of singularities)
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1 NaN NaN NaN
## date NA NA NA NA
##
## Residual standard error: NaN on 0 degrees of freedom
##
## [1] "**************************************************"
## [1] "**************************************************"
## [1] " >>>> Market Segment: Offline TA/TO Country: IRL"
##
## Call:
## lm(formula = paste(dependent_variable, "~ date"), data = subset_data)
##
## Residuals:
## Min 1Q Median 3Q Max
## 0 0 0 0 0
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0 0 NaN NaN
## date 0 0 NaN NaN
##
## Residual standard error: 0 on 18 degrees of freedom
## Multiple R-squared: NaN, Adjusted R-squared: NaN
## F-statistic: NaN on 1 and 18 DF, p-value: NA
##
## [1] "**************************************************"
## [1] "**************************************************"
## [1] " >>>> Market Segment: Offline TA/TO Country: USA"
##
## Call:
## lm(formula = paste(dependent_variable, "~ date"), data = subset_data)
##
## Residuals:
## ALL 1 residuals are 0: no residual degrees of freedom!
##
## Coefficients: (1 not defined because of singularities)
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0 NaN NaN NaN
## date NA NA NA NA
##
## Residual standard error: NaN on 0 degrees of freedom
##
## [1] "**************************************************"
## [1] "**************************************************"
## [1] " >>>> Market Segment: Offline TA/TO Country: FRA"
##
## Call:
## lm(formula = paste(dependent_variable, "~ date"), data = subset_data)
##
## Residuals:
## 30 113 224 256 325 368
## 0 0 0 0 0 0
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0 0 NaN NaN
## date 0 0 NaN NaN
##
## Residual standard error: 0 on 4 degrees of freedom
## Multiple R-squared: NaN, Adjusted R-squared: NaN
## F-statistic: NaN on 1 and 4 DF, p-value: NA
##
## [1] "**************************************************"
## [1] "**************************************************"
## [1] " >>>> Market Segment: Offline TA/TO Country: ESP"
##
## Call:
## lm(formula = paste(dependent_variable, "~ date"), data = subset_data)
##
## Residuals:
## Min 1Q Median 3Q Max
## 0 0 0 0 0
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0 0 NaN NaN
## date 0 0 NaN NaN
##
## Residual standard error: 0 on 8 degrees of freedom
## Multiple R-squared: NaN, Adjusted R-squared: NaN
## F-statistic: NaN on 1 and 8 DF, p-value: NA
##
## [1] "**************************************************"
## [1] "**************************************************"
## [1] " >>>> Market Segment: Offline TA/TO Country: ITA"
##
## Call:
## lm(formula = paste(dependent_variable, "~ date"), data = subset_data)
##
## Residuals:
## ALL 2 residuals are 0: no residual degrees of freedom!
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0 NaN NaN NaN
## date 0 NaN NaN NaN
##
## Residual standard error: NaN on 0 degrees of freedom
## Multiple R-squared: NaN, Adjusted R-squared: NaN
## F-statistic: NaN on 1 and 0 DF, p-value: NA
##
## [1] "**************************************************"
## [1] "No data for Market Segment: Offline TA/TO Country: DNK"
## [1] "**************************************************"
## [1] " >>>> Market Segment: Offline TA/TO Country: SWE"
##
## Call:
## lm(formula = paste(dependent_variable, "~ date"), data = subset_data)
##
## Residuals:
## ALL 1 residuals are 0: no residual degrees of freedom!
##
## Coefficients: (1 not defined because of singularities)
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0 NaN NaN NaN
## date NA NA NA NA
##
## Residual standard error: NaN on 0 degrees of freedom
##
## [1] "**************************************************"
## [1] "**************************************************"
## [1] " >>>> Market Segment: Offline TA/TO Country: RUS"
##
## Call:
## lm(formula = paste(dependent_variable, "~ date"), data = subset_data)
##
## Residuals:
## ALL 1 residuals are 0: no residual degrees of freedom!
##
## Coefficients: (1 not defined because of singularities)
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0 NaN NaN NaN
## date NA NA NA NA
##
## Residual standard error: NaN on 0 degrees of freedom
##
## [1] "**************************************************"
## [1] "**************************************************"
## [1] " >>>> Market Segment: Offline TA/TO Country: MAR"
##
## Call:
## lm(formula = paste(dependent_variable, "~ date"), data = subset_data)
##
## Residuals:
## ALL 1 residuals are 0: no residual degrees of freedom!
##
## Coefficients: (1 not defined because of singularities)
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0 NaN NaN NaN
## date NA NA NA NA
##
## Residual standard error: NaN on 0 degrees of freedom
##
## [1] "**************************************************"
## [1] "No data for Market Segment: Offline TA/TO Country: ROU"
## [1] "**************************************************"
## [1] " >>>> Market Segment: Offline TA/TO Country: NLD"
##
## Call:
## lm(formula = paste(dependent_variable, "~ date"), data = subset_data)
##
## Residuals:
## 171 237 312 380 431
## 0 0 0 0 0
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0 0 NaN NaN
## date 0 0 NaN NaN
##
## Residual standard error: 0 on 3 degrees of freedom
## Multiple R-squared: NaN, Adjusted R-squared: NaN
## F-statistic: NaN on 1 and 3 DF, p-value: NA
##
## [1] "**************************************************"
## [1] "No data for Market Segment: Offline TA/TO Country: UKR"
## [1] "**************************************************"
## [1] " >>>> Market Segment: Offline TA/TO Country: DEU"
##
## Call:
## lm(formula = paste(dependent_variable, "~ date"), data = subset_data)
##
## Residuals:
## 271 394 413 444 530 907
## 0 0 0 0 0 0
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0 0 NaN NaN
## date 0 0 NaN NaN
##
## Residual standard error: 0 on 4 degrees of freedom
## Multiple R-squared: NaN, Adjusted R-squared: NaN
## F-statistic: NaN on 1 and 4 DF, p-value: NA
##
## [1] "**************************************************"
## [1] "**************************************************"
## [1] " >>>> Market Segment: Offline TA/TO Country: POL"
##
## Call:
## lm(formula = paste(dependent_variable, "~ date"), data = subset_data)
##
## Residuals:
## 180 209 306 400 466 479 510
## 0 0 0 0 0 0 0
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0 0 NaN NaN
## date 0 0 NaN NaN
##
## Residual standard error: 0 on 5 degrees of freedom
## Multiple R-squared: NaN, Adjusted R-squared: NaN
## F-statistic: NaN on 1 and 5 DF, p-value: NA
##
## [1] "**************************************************"
## [1] "**************************************************"
## [1] " >>>> Market Segment: Offline TA/TO Country: BEL"
##
## Call:
## lm(formula = paste(dependent_variable, "~ date"), data = subset_data)
##
## Residuals:
## ALL 1 residuals are 0: no residual degrees of freedom!
##
## Coefficients: (1 not defined because of singularities)
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0 NaN NaN NaN
## date NA NA NA NA
##
## Residual standard error: NaN on 0 degrees of freedom
##
## [1] "**************************************************"
## [1] "**************************************************"
## [1] " >>>> Market Segment: Offline TA/TO Country: FIN"
##
## Call:
## lm(formula = paste(dependent_variable, "~ date"), data = subset_data)
##
## Residuals:
## ALL 1 residuals are 0: no residual degrees of freedom!
##
## Coefficients: (1 not defined because of singularities)
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0 NaN NaN NaN
## date NA NA NA NA
##
## Residual standard error: NaN on 0 degrees of freedom
##
## [1] "**************************************************"
## [1] "No data for Market Segment: Offline TA/TO Country: CHE"
## [1] "No data for Market Segment: Offline TA/TO Country: AUT"
## [1] "**************************************************"
## [1] " >>>> Market Segment: Offline TA/TO Country: DZA"
##
## Call:
## lm(formula = paste(dependent_variable, "~ date"), data = subset_data)
##
## Residuals:
## ALL 1 residuals are 0: no residual degrees of freedom!
##
## Coefficients: (1 not defined because of singularities)
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0 NaN NaN NaN
## date NA NA NA NA
##
## Residual standard error: NaN on 0 degrees of freedom
##
## [1] "**************************************************"
## [1] "No data for Market Segment: Offline TA/TO Country: ZAF"
## [1] "**************************************************"
## [1] " >>>> Market Segment: Offline TA/TO Country: CN"
##
## Call:
## lm(formula = paste(dependent_variable, "~ date"), data = subset_data)
##
## Residuals:
## ALL 2 residuals are 0: no residual degrees of freedom!
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0 NaN NaN NaN
## date 0 NaN NaN NaN
##
## Residual standard error: NaN on 0 degrees of freedom
## Multiple R-squared: NaN, Adjusted R-squared: NaN
## F-statistic: NaN on 1 and 0 DF, p-value: NA
##
## [1] "**************************************************"
## [1] "No data for Market Segment: Offline TA/TO Country: KOR"
## [1] "**************************************************"
## [1] " >>>> Market Segment: Offline TA/TO Country: LUX"
##
## Call:
## lm(formula = paste(dependent_variable, "~ date"), data = subset_data)
##
## Residuals:
## ALL 1 residuals are 0: no residual degrees of freedom!
##
## Coefficients: (1 not defined because of singularities)
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0 NaN NaN NaN
## date NA NA NA NA
##
## Residual standard error: NaN on 0 degrees of freedom
##
## [1] "**************************************************"
## [1] "No data for Market Segment: Offline TA/TO Country: ARE"
## [1] "No data for Market Segment: Offline TA/TO Country: BRA"
## [1] "**************************************************"
## [1] " >>>> Market Segment: Offline TA/TO Country: JAM"
##
## Call:
## lm(formula = paste(dependent_variable, "~ date"), data = subset_data)
##
## Residuals:
## ALL 1 residuals are 0: no residual degrees of freedom!
##
## Coefficients: (1 not defined because of singularities)
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0 NaN NaN NaN
## date NA NA NA NA
##
## Residual standard error: NaN on 0 degrees of freedom
##
## [1] "**************************************************"
## [1] "No data for Market Segment: Offline TA/TO Country: AUS"
## [1] "No data for Market Segment: Offline TA/TO Country: NOR"
## [1] "No data for Market Segment: Offline TA/TO Country: ISR"
## [1] "No data for Market Segment: Offline TA/TO Country: CHN"
## [1] "**************************************************"
## [1] " >>>> Market Segment: Offline TA/TO Country: LTU"
##
## Call:
## lm(formula = paste(dependent_variable, "~ date"), data = subset_data)
##
## Residuals:
## ALL 1 residuals are 0: no residual degrees of freedom!
##
## Coefficients: (1 not defined because of singularities)
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0 NaN NaN NaN
## date NA NA NA NA
##
## Residual standard error: NaN on 0 degrees of freedom
##
## [1] "**************************************************"
## [1] "**************************************************"
## [1] " >>>> Market Segment: Offline TA/TO Country: EST"
##
## Call:
## lm(formula = paste(dependent_variable, "~ date"), data = subset_data)
##
## Residuals:
## ALL 1 residuals are 0: no residual degrees of freedom!
##
## Coefficients: (1 not defined because of singularities)
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0 NaN NaN NaN
## date NA NA NA NA
##
## Residual standard error: NaN on 0 degrees of freedom
##
## [1] "**************************************************"
## [1] "No data for Market Segment: Offline TA/TO Country: GGY"
## [1] "No data for Market Segment: Offline TA/TO Country: NGA"
## [1] "No data for Market Segment: Offline TA/TO Country: COL"
## [1] "No data for Market Segment: Offline TA/TO Country: AGO"
## [1] "No data for Market Segment: Offline TA/TO Country: PHL"
## [1] "No data for Market Segment: Offline TA/TO Country: JPN"
## [1] "**************************************************"
## [1] " >>>> Market Segment: Corporate Country: PRT"
##
## Call:
## lm(formula = paste(dependent_variable, "~ date"), data = subset_data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.95220 0.08454 0.13169 0.19925 0.37322
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -8.8418793 5.2446995 -1.686 0.0992 .
## date 0.0005630 0.0003064 1.838 0.0732 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.3972 on 42 degrees of freedom
## Multiple R-squared: 0.07442, Adjusted R-squared: 0.05238
## F-statistic: 3.377 on 1 and 42 DF, p-value: 0.07319
##
## [1] "**************************************************"
## [1] "**************************************************"
## [1] " >>>> Market Segment: Corporate Country: GBR"
##
## Call:
## lm(formula = paste(dependent_variable, "~ date"), data = subset_data)
##
## Residuals:
## ALL 1 residuals are 0: no residual degrees of freedom!
##
## Coefficients: (1 not defined because of singularities)
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0 NaN NaN NaN
## date NA NA NA NA
##
## Residual standard error: NaN on 0 degrees of freedom
##
## [1] "**************************************************"
## [1] "No data for Market Segment: Corporate Country: NULL"
## [1] "No data for Market Segment: Corporate Country: IRL"
## [1] "No data for Market Segment: Corporate Country: USA"
## [1] "No data for Market Segment: Corporate Country: FRA"
## [1] "No data for Market Segment: Corporate Country: ESP"
## [1] "**************************************************"
## [1] " >>>> Market Segment: Corporate Country: ITA"
##
## Call:
## lm(formula = paste(dependent_variable, "~ date"), data = subset_data)
##
## Residuals:
## ALL 1 residuals are 0: no residual degrees of freedom!
##
## Coefficients: (1 not defined because of singularities)
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0 NaN NaN NaN
## date NA NA NA NA
##
## Residual standard error: NaN on 0 degrees of freedom
##
## [1] "**************************************************"
## [1] "No data for Market Segment: Corporate Country: DNK"
## [1] "No data for Market Segment: Corporate Country: SWE"
## [1] "No data for Market Segment: Corporate Country: RUS"
## [1] "No data for Market Segment: Corporate Country: MAR"
## [1] "No data for Market Segment: Corporate Country: ROU"
## [1] "No data for Market Segment: Corporate Country: NLD"
## [1] "No data for Market Segment: Corporate Country: UKR"
## [1] "No data for Market Segment: Corporate Country: DEU"
## [1] "No data for Market Segment: Corporate Country: POL"
## [1] "**************************************************"
## [1] " >>>> Market Segment: Corporate Country: BEL"
##
## Call:
## lm(formula = paste(dependent_variable, "~ date"), data = subset_data)
##
## Residuals:
## ALL 1 residuals are 0: no residual degrees of freedom!
##
## Coefficients: (1 not defined because of singularities)
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0 NaN NaN NaN
## date NA NA NA NA
##
## Residual standard error: NaN on 0 degrees of freedom
##
## [1] "**************************************************"
## [1] "No data for Market Segment: Corporate Country: FIN"
## [1] "No data for Market Segment: Corporate Country: CHE"
## [1] "**************************************************"
## [1] " >>>> Market Segment: Corporate Country: AUT"
##
## Call:
## lm(formula = paste(dependent_variable, "~ date"), data = subset_data)
##
## Residuals:
## ALL 1 residuals are 0: no residual degrees of freedom!
##
## Coefficients: (1 not defined because of singularities)
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0 NaN NaN NaN
## date NA NA NA NA
##
## Residual standard error: NaN on 0 degrees of freedom
##
## [1] "**************************************************"
## [1] "No data for Market Segment: Corporate Country: DZA"
## [1] "No data for Market Segment: Corporate Country: ZAF"
## [1] "No data for Market Segment: Corporate Country: CN"
## [1] "No data for Market Segment: Corporate Country: KOR"
## [1] "No data for Market Segment: Corporate Country: LUX"
## [1] "No data for Market Segment: Corporate Country: ARE"
## [1] "No data for Market Segment: Corporate Country: BRA"
## [1] "No data for Market Segment: Corporate Country: JAM"
## [1] "No data for Market Segment: Corporate Country: AUS"
## [1] "No data for Market Segment: Corporate Country: NOR"
## [1] "No data for Market Segment: Corporate Country: ISR"
## [1] "No data for Market Segment: Corporate Country: CHN"
## [1] "No data for Market Segment: Corporate Country: LTU"
## [1] "No data for Market Segment: Corporate Country: EST"
## [1] "No data for Market Segment: Corporate Country: GGY"
## [1] "No data for Market Segment: Corporate Country: NGA"
## [1] "No data for Market Segment: Corporate Country: COL"
## [1] "No data for Market Segment: Corporate Country: AGO"
## [1] "No data for Market Segment: Corporate Country: PHL"
## [1] "No data for Market Segment: Corporate Country: JPN"## Warning in summary.lm(lm_model): essentially perfect fit: summary may be
## unreliable## [1] "**************************************************"
## [1] " >>>> Market Segment: Groups Country: PRT"## Warning in summary.lm(lm_model): essentially perfect fit: summary may be
## unreliable##
## Call:
## lm(formula = paste(dependent_variable, "~ date"), data = subset_data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -8.881e-16 2.474e-17 2.997e-17 4.200e-17 1.360e-16
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1.000e+00 2.993e-15 3.341e+14 <2e-16 ***
## date 1.307e-19 1.760e-19 7.430e-01 0.468
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 2.249e-16 on 17 degrees of freedom
## Multiple R-squared: 0.4937, Adjusted R-squared: 0.4639
## F-statistic: 16.58 on 1 and 17 DF, p-value: 0.0007943
##
## [1] "**************************************************"
## [1] "**************************************************"
## [1] " >>>> Market Segment: Groups Country: GBR"
##
## Call:
## lm(formula = paste(dependent_variable, "~ date"), data = subset_data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.20890 -0.06795 0.05353 0.05890 0.20314
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 22.8870058 3.2084459 7.133 5.07e-06 ***
## date -0.0013424 0.0001887 -7.114 5.22e-06 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.1205 on 14 degrees of freedom
## Multiple R-squared: 0.7833, Adjusted R-squared: 0.7678
## F-statistic: 50.61 on 1 and 14 DF, p-value: 5.219e-06
##
## [1] "**************************************************"
## [1] "No data for Market Segment: Groups Country: NULL"
## [1] "**************************************************"
## [1] " >>>> Market Segment: Groups Country: IRL"
##
## Call:
## lm(formula = paste(dependent_variable, "~ date"), data = subset_data)
##
## Residuals:
## ALL 1 residuals are 0: no residual degrees of freedom!
##
## Coefficients: (1 not defined because of singularities)
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0 NaN NaN NaN
## date NA NA NA NA
##
## Residual standard error: NaN on 0 degrees of freedom
##
## [1] "**************************************************"
## [1] "No data for Market Segment: Groups Country: USA"
## [1] "**************************************************"
## [1] " >>>> Market Segment: Groups Country: FRA"
##
## Call:
## lm(formula = paste(dependent_variable, "~ date"), data = subset_data)
##
## Residuals:
## ALL 1 residuals are 0: no residual degrees of freedom!
##
## Coefficients: (1 not defined because of singularities)
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0 NaN NaN NaN
## date NA NA NA NA
##
## Residual standard error: NaN on 0 degrees of freedom
##
## [1] "**************************************************"
## [1] "**************************************************"
## [1] " >>>> Market Segment: Groups Country: ESP"
##
## Call:
## lm(formula = paste(dependent_variable, "~ date"), data = subset_data)
##
## Residuals:
## 240 301 320
## 0 0 0
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0 0 NaN NaN
## date 0 0 NaN NaN
##
## Residual standard error: 0 on 1 degrees of freedom
## Multiple R-squared: NaN, Adjusted R-squared: NaN
## F-statistic: NaN on 1 and 1 DF, p-value: NA
##
## [1] "**************************************************"
## [1] "No data for Market Segment: Groups Country: ITA"
## [1] "No data for Market Segment: Groups Country: DNK"
## [1] "No data for Market Segment: Groups Country: SWE"
## [1] "No data for Market Segment: Groups Country: RUS"
## [1] "No data for Market Segment: Groups Country: MAR"
## [1] "No data for Market Segment: Groups Country: ROU"
## [1] "**************************************************"
## [1] " >>>> Market Segment: Groups Country: NLD"
##
## Call:
## lm(formula = paste(dependent_variable, "~ date"), data = subset_data)
##
## Residuals:
## ALL 1 residuals are 0: no residual degrees of freedom!
##
## Coefficients: (1 not defined because of singularities)
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0 NaN NaN NaN
## date NA NA NA NA
##
## Residual standard error: NaN on 0 degrees of freedom
##
## [1] "**************************************************"
## [1] "No data for Market Segment: Groups Country: UKR"
## [1] "**************************************************"
## [1] " >>>> Market Segment: Groups Country: DEU"
##
## Call:
## lm(formula = paste(dependent_variable, "~ date"), data = subset_data)
##
## Residuals:
## ALL 1 residuals are 0: no residual degrees of freedom!
##
## Coefficients: (1 not defined because of singularities)
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0 NaN NaN NaN
## date NA NA NA NA
##
## Residual standard error: NaN on 0 degrees of freedom
##
## [1] "**************************************************"
## [1] "No data for Market Segment: Groups Country: POL"
## [1] "**************************************************"
## [1] " >>>> Market Segment: Groups Country: BEL"
##
## Call:
## lm(formula = paste(dependent_variable, "~ date"), data = subset_data)
##
## Residuals:
## ALL 2 residuals are 0: no residual degrees of freedom!
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 24.509246 NaN NaN NaN
## date -0.001422 NaN NaN NaN
##
## Residual standard error: NaN on 0 degrees of freedom
## Multiple R-squared: 1, Adjusted R-squared: NaN
## F-statistic: NaN on 1 and 0 DF, p-value: NA
##
## [1] "**************************************************"
## [1] "No data for Market Segment: Groups Country: FIN"
## [1] "No data for Market Segment: Groups Country: CHE"
## [1] "No data for Market Segment: Groups Country: AUT"
## [1] "No data for Market Segment: Groups Country: DZA"
## [1] "No data for Market Segment: Groups Country: ZAF"
## [1] "No data for Market Segment: Groups Country: CN"
## [1] "No data for Market Segment: Groups Country: KOR"
## [1] "No data for Market Segment: Groups Country: LUX"
## [1] "No data for Market Segment: Groups Country: ARE"
## [1] "No data for Market Segment: Groups Country: BRA"
## [1] "No data for Market Segment: Groups Country: JAM"
## [1] "No data for Market Segment: Groups Country: AUS"
## [1] "No data for Market Segment: Groups Country: NOR"
## [1] "No data for Market Segment: Groups Country: ISR"
## [1] "No data for Market Segment: Groups Country: CHN"
## [1] "No data for Market Segment: Groups Country: LTU"
## [1] "No data for Market Segment: Groups Country: EST"
## [1] "No data for Market Segment: Groups Country: GGY"
## [1] "No data for Market Segment: Groups Country: NGA"
## [1] "No data for Market Segment: Groups Country: COL"
## [1] "No data for Market Segment: Groups Country: AGO"
## [1] "No data for Market Segment: Groups Country: PHL"
## [1] "No data for Market Segment: Groups Country: JPN"
## [1] "**************************************************"
## [1] " >>>> Market Segment: Complementary Country: PRT"
##
## Call:
## lm(formula = paste(dependent_variable, "~ date"), data = subset_data)
##
## Residuals:
## 652 680 706 724 759 781
## 0.09839 0.12468 0.14768 -0.81946 0.21340 0.23531
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 19.746065 72.672185 0.272 0.799
## date -0.001095 0.004209 -0.260 0.808
##
## Residual standard error: 0.4526 on 4 degrees of freedom
## Multiple R-squared: 0.01665, Adjusted R-squared: -0.2292
## F-statistic: 0.06773 on 1 and 4 DF, p-value: 0.8075
##
## [1] "**************************************************"
## [1] "No data for Market Segment: Complementary Country: GBR"
## [1] "No data for Market Segment: Complementary Country: NULL"
## [1] "No data for Market Segment: Complementary Country: IRL"
## [1] "No data for Market Segment: Complementary Country: USA"
## [1] "No data for Market Segment: Complementary Country: FRA"
## [1] "No data for Market Segment: Complementary Country: ESP"
## [1] "No data for Market Segment: Complementary Country: ITA"
## [1] "No data for Market Segment: Complementary Country: DNK"
## [1] "No data for Market Segment: Complementary Country: SWE"
## [1] "No data for Market Segment: Complementary Country: RUS"
## [1] "No data for Market Segment: Complementary Country: MAR"
## [1] "No data for Market Segment: Complementary Country: ROU"
## [1] "No data for Market Segment: Complementary Country: NLD"
## [1] "No data for Market Segment: Complementary Country: UKR"
## [1] "No data for Market Segment: Complementary Country: DEU"
## [1] "No data for Market Segment: Complementary Country: POL"
## [1] "No data for Market Segment: Complementary Country: BEL"
## [1] "No data for Market Segment: Complementary Country: FIN"
## [1] "No data for Market Segment: Complementary Country: CHE"
## [1] "No data for Market Segment: Complementary Country: AUT"
## [1] "No data for Market Segment: Complementary Country: DZA"
## [1] "No data for Market Segment: Complementary Country: ZAF"
## [1] "No data for Market Segment: Complementary Country: CN"
## [1] "No data for Market Segment: Complementary Country: KOR"
## [1] "No data for Market Segment: Complementary Country: LUX"
## [1] "No data for Market Segment: Complementary Country: ARE"
## [1] "No data for Market Segment: Complementary Country: BRA"
## [1] "No data for Market Segment: Complementary Country: JAM"
## [1] "No data for Market Segment: Complementary Country: AUS"
## [1] "No data for Market Segment: Complementary Country: NOR"
## [1] "No data for Market Segment: Complementary Country: ISR"
## [1] "No data for Market Segment: Complementary Country: CHN"
## [1] "No data for Market Segment: Complementary Country: LTU"
## [1] "No data for Market Segment: Complementary Country: EST"
## [1] "No data for Market Segment: Complementary Country: GGY"
## [1] "No data for Market Segment: Complementary Country: NGA"
## [1] "No data for Market Segment: Complementary Country: COL"
## [1] "No data for Market Segment: Complementary Country: AGO"
## [1] "No data for Market Segment: Complementary Country: PHL"
## [1] "No data for Market Segment: Complementary Country: JPN"Explanation :
In Above linear regression result,1. There are some data for selected
option - market segment and Country where data is not avaliable.
2.
The model indicates a significant intercept term but the coefficient for
the date variable is not significant (p-value is verying).
3. The
R-squared value is low means model explain small proportion of the
variance in the “is_canceled” dependent variable.
4. In summary, The
linear regression results vary across different subsets of the data.
Some subsets exhibit significant coefficients while others have issues
such as all residuals being 0 or missing data.
decomp <- ma(ts_data$response_variable, order = 12)
plot(decomp)
Above graph is reprsentating curve(/fluctuations) obtained from the
moving average for the given period window.This graph can be used to
determine the pattern in the data.
acf(ts_data$response_variable, lag.max = 50)
acf : is autocorrelation Function which measures the correlation
between a time series and a lagged version. this is displaying the
autocorrelation coefficients for different lags.This graph can help to
determine the potential seasonal patterns which can further use for
business modeling and forecasting.
# Plot PACF
pacf(ts_data$response_variable, lag.max = 100)
Partial Autocorrelation Function (PACF) which measures the correlation between a series and a lagged version of itself after removing the effects of intermediate lags. This graph can be used to find the temporal patterns and dependencies present in the data which can be further used to understand underlying dynamics and potentially informing forecasting or modeling.