Question 1: Read in the gambling dataset check the first couple of rows and describe the data types. Identify incorrect data types, if any. ( 5 Points )

gamblingdata = read.csv(file="data/gambling.csv")
head(gamblingdata)

Question 2: Describe the data using full sentences and using descriptive statistics. ( 5 Points )

summary(gamblingdata)
      sex             status          income           verbal          gamble     
 Min.   :0.0000   Min.   :18.00   Min.   : 0.600   Min.   : 1.00   Min.   :  0.0  
 1st Qu.:0.0000   1st Qu.:28.00   1st Qu.: 2.000   1st Qu.: 6.00   1st Qu.:  1.1  
 Median :0.0000   Median :43.00   Median : 3.250   Median : 7.00   Median :  6.0  
 Mean   :0.4043   Mean   :45.23   Mean   : 4.642   Mean   : 6.66   Mean   : 19.3  
 3rd Qu.:1.0000   3rd Qu.:61.50   3rd Qu.: 6.210   3rd Qu.: 8.00   3rd Qu.: 19.4  
 Max.   :1.0000   Max.   :75.00   Max.   :15.000   Max.   :10.00   Max.   :156.0

The data in the gambling.csv show gender of players, status, income, verbal signals, and rate of gamble.

meanIncome = mean(gamblingdata$income)
meanIncome
[1] 4.641915
maxIncome = max(gamblingdata$income)
maxIncome
[1] 15
minIncome = min(gamblingdata$income)
minIncome
[1] 0.6
medianIncome = median(gamblingdata$income)
medianIncome
[1] 3.25

Question 3: Estimate the upper and lower threshold for the verbal score ( 5 Points )

HINT: A common way to estimate the upper and lower threshold is to take the mean (+ or -) 3 * standard deviation.

verbal = gamblingdata$verbal
verbal
 [1]  8  8  6  4  8  6  7  5  6  7  6  6  4  6  6  8  8  5  8  9  8  9  5  4  7  7  4  6  7  8  2  7  7 10  1  8
[37]  7  6  6  6  9  9  8  9  6  7  9
verbalmean = mean(verbal)
verbalmean
[1] 6.659574
verbalsd = sd(verbal)
verbalsd
[1] 1.856558
lowerverbal = verbalmean -(3 * verbalsd)
upperverbal = verbalmean + (3 * verbalsd)
lowerverbal
[1] 1.0899
upperverbal
[1] 12.22925

Question 4: Calculate the z-score for income where x=13. Based on the income value x=13 pounds per week, how would you rate the income: low income, average income, high income. Why? ( 5 Points )

Hint: zscore = (x - mean)/sd

income = gamblingdata$income
income
 [1]  2.00  2.50  2.00  7.00  2.00  3.47  5.50  6.42  2.00  6.00  3.00  4.75  2.20  2.00  3.00  1.50  9.50 10.00
[19]  4.00  3.50  3.00  2.50  3.50 10.00  6.50  1.50  5.44  1.00  0.60  5.50 12.00  7.00 15.00  2.00  1.50  4.50
[37]  2.50  8.00 10.00  1.60  2.00 15.00  3.00  3.25  4.94  1.50  2.50
incomemean = mean(income)
incomemean
[1] 4.641915
incomesd = sd(income)
incomesd
[1] 3.551371
zscore = (13-incomemean)/incomesd
zscore
[1] 2.353481

Question 5: Create a histogram for the zscore of income. What do you notice about the shape? ( 5 Points )

Hint: To plot a histogram, use the function hist(variable).

zscoresIncome = (income - meanIncome)/incomesd
hist(zscoresIncome)

this histogram has a negative slope. there are a lot of negative zscores compared to positives.

Question 6: Analyze the correlation plot below. Give relavant information about the negative correlated, no correlared and positive correlated variables. ( 5 Points )

The positives outweigh the negatives in seeing that the x axis is more distinguish on the right hand side. There is a frequent correlation relating to the casual hypothesis notated with the dots going through the middle in a diagonal line.

Extra Credit: Analyze the correlation table below. Give relavant information about the negative correlated, no correlared and positive correlated variables. ( 5 Points )

# Create a correlation table "cor(movies)"
moviesdata = read.csv(file="data/movies.csv")
head(moviesdata)
cor(moviesdata)
               length budget  director actor1 actor2 actor3 cast_likes  fb_likes critic_reviews users_reviews
length              1     NA        NA     NA     NA     NA         NA        NA             NA            NA
budget             NA      1        NA     NA     NA     NA         NA        NA             NA            NA
director           NA     NA 1.0000000     NA     NA     NA  0.1858875 0.2894939             NA            NA
actor1             NA     NA        NA      1     NA     NA         NA        NA             NA            NA
actor2             NA     NA        NA     NA      1     NA         NA        NA             NA            NA
actor3             NA     NA        NA     NA     NA      1         NA        NA             NA            NA
cast_likes         NA     NA 0.1858875     NA     NA     NA  1.0000000 0.3387454             NA            NA
fb_likes           NA     NA 0.2894939     NA     NA     NA  0.3387454 1.0000000             NA            NA
critic_reviews     NA     NA        NA     NA     NA     NA         NA        NA              1            NA
users_reviews      NA     NA        NA     NA     NA     NA         NA        NA             NA             1
users_votes        NA     NA 0.3492878     NA     NA     NA  0.4140989 0.8001157             NA            NA
score              NA     NA 0.1765288     NA     NA     NA  0.1484501 0.4604384             NA            NA
gross              NA     NA 0.1717334     NA     NA     NA  0.3829801 0.5644529             NA            NA
               users_votes     score     gross
length                  NA        NA        NA
budget                  NA        NA        NA
director         0.3492878 0.1765288 0.1717334
actor1                  NA        NA        NA
actor2                  NA        NA        NA
actor3                  NA        NA        NA
cast_likes       0.4140989 0.1484501 0.3829801
fb_likes         0.8001157 0.4604384 0.5644529
critic_reviews          NA        NA        NA
users_reviews           NA        NA        NA
users_votes      1.0000000 0.4742893 0.6892893
score            0.4742893 1.0000000 0.2669350
gross            0.6892893 0.2669350 1.0000000
cor(moviesdata)
               length budget  director actor1 actor2 actor3 cast_likes  fb_likes critic_reviews users_reviews users_votes
length              1     NA        NA     NA     NA     NA         NA        NA             NA            NA          NA
budget             NA      1        NA     NA     NA     NA         NA        NA             NA            NA          NA
director           NA     NA 1.0000000     NA     NA     NA  0.1858875 0.2894939             NA            NA   0.3492878
actor1             NA     NA        NA      1     NA     NA         NA        NA             NA            NA          NA
actor2             NA     NA        NA     NA      1     NA         NA        NA             NA            NA          NA
actor3             NA     NA        NA     NA     NA      1         NA        NA             NA            NA          NA
cast_likes         NA     NA 0.1858875     NA     NA     NA  1.0000000 0.3387454             NA            NA   0.4140989
fb_likes           NA     NA 0.2894939     NA     NA     NA  0.3387454 1.0000000             NA            NA   0.8001157
critic_reviews     NA     NA        NA     NA     NA     NA         NA        NA              1            NA          NA
users_reviews      NA     NA        NA     NA     NA     NA         NA        NA             NA             1          NA
users_votes        NA     NA 0.3492878     NA     NA     NA  0.4140989 0.8001157             NA            NA   1.0000000
score              NA     NA 0.1765288     NA     NA     NA  0.1484501 0.4604384             NA            NA   0.4742893
gross              NA     NA 0.1717334     NA     NA     NA  0.3829801 0.5644529             NA            NA   0.6892893
                   score     gross
length                NA        NA
budget                NA        NA
director       0.1765288 0.1717334
actor1                NA        NA
actor2                NA        NA
actor3                NA        NA
cast_likes     0.1484501 0.3829801
fb_likes       0.4604384 0.5644529
critic_reviews        NA        NA
users_reviews         NA        NA
users_votes    0.4742893 0.6892893
score          1.0000000 0.2669350
gross          0.2669350 1.0000000
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