Mutual Fund Performance Model
Monesh Sharma
March 29, 2018
Problem Definition
Use data set of Mutual Funds.
1. Analysis of Mutual Fund Data of different scheme.(Summarize, plots etc.)
2. Visualization of Data
3. Some T and Chi square tests through data
4. Correlation between dependent and Independent variables
5. Find out which all columns / features impact Price of hotel room
6. Predict the hotel prices with some dummy values.
Attributes:
Dataset is of different mutual fund schemes in India:
Dependent Variable
1YearReturn - Annual Return by scheme
Independent Variables
Investment Style
Market Cap
Turnover
Net Assets (Cr)
Standard Deviation
Sharpe Ratio
Sortino Ratio
Beta
Alpha
R-Squared
Expense Ratio
Tenure 1
Tenure 2
Tenure3
Setup
library(tidyr)
library(dplyr)##
## Attaching package: 'dplyr'## The following objects are masked from 'package:stats':
##
## filter, lag## The following objects are masked from 'package:base':
##
## intersect, setdiff, setequal, unionlibrary(ggplot2)
library(corrgram)
library(gridExtra) ##
## Attaching package: 'gridExtra'## The following object is masked from 'package:dplyr':
##
## combinelibrary(vcd)## Loading required package: gridlibrary(psych)##
## Attaching package: 'psych'## The following objects are masked from 'package:ggplot2':
##
## %+%, alphalibrary(car)##
## Attaching package: 'car'## The following object is masked from 'package:psych':
##
## logit## The following object is masked from 'package:dplyr':
##
## recodelibrary(corrplot)
library(coefplot)Functions
detect_outliers <- function(inp, na.rm=TRUE) {
i.qnt <- quantile(inp, probs=c(.25, .75), na.rm=na.rm)
i.max <- 1.5 * IQR(inp, na.rm=na.rm)
otp <- inp
otp[inp < (i.qnt[1] - i.max)] <- NA
otp[inp > (i.qnt[2] + i.max)] <- NA
#inp <- count(inp[is.na(otp)])
sum(is.na(otp))
}
Non_outliers <- function(x, na.rm = TRUE, ...) {
qnt <- quantile(x, probs=c(.25, .75), na.rm = na.rm, ...)
H <- 1.5 * IQR(x, na.rm = na.rm)
y <- x
y[x < (qnt[1] - H)] <- NA
y[x > (qnt[2] + H)] <- NA
y
}
Remove_Outliers <- function ( z, na.rm = TRUE){
Out <- Non_outliers(z)
Out <-as.data.frame (Out)
z <- Out$Out[match(z, Out$Out)]
z
}
Graph_Boxplot <- function (input, na.rm = TRUE){
Plot <- ggplot(dfrModel, aes(x="", y=input)) +
geom_boxplot(aes(fill=input), color="green") +
labs(title="Outliers")
Plot
}Dataset
dfrModel <- read.csv("D:/Welingkar/Trim 6/Data/Regression_data.csv", header=T, stringsAsFactors=F)
intRowCount <- nrow(dfrModel)
head(dfrModel)## X1.Year.Return Investment.Style Market.Cap Turnover Net.Assets..Cr.
## 1 12.90 1 66337.65 62 5819.08
## 2 14.35 1 66337.65 62 5819.08
## 3 16.39 2 50546.68 24 1453.04
## 4 14.86 2 50546.68 24 1453.04
## 5 11.32 1 63907.70 49 8602.25
## 6 12.67 1 63907.70 49 8602.25
## Standard.Deviation Sharpe.Ratio Sortino.Ratio Beta Alpha R.Squared
## 1 15.51 0.62 1.00 0.97 5.00 0.73
## 2 15.52 0.69 1.12 0.97 6.19 0.73
## 3 19.36 0.74 1.11 0.89 8.70 0.79
## 4 19.35 0.69 1.04 0.89 7.75 0.78
## 5 14.30 0.71 1.09 0.94 5.69 0.81
## 6 14.32 0.78 1.20 0.94 6.70 0.81
## Expense.Ratio Tenure.1 Tenure.2 Tenure3
## 1 2.30 6.4 0.0 0
## 2 1.00 5.2 0.0 0
## 3 1.15 4.3 2.6 0
## 4 2.45 4.3 2.6 0
## 5 2.23 5.5 0.0 0
## 6 0.99 5.2 0.0 0Observation 1. There are total ‘intRowCount’ data records in the file.
As there are Non Numeric data as well in the given dataset, so we are going to remove the non numeric data.
Summary
lapply(dfrModel, FUN=describe)## $X1.Year.Return
## vars n mean sd median trimmed mad min max range skew kurtosis
## X1 1 164 17.76 6.02 16.78 17.12 5.17 6.1 37.93 31.83 1.06 1.44
## se
## X1 0.47
##
## $Investment.Style
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 164 1.29 0.48 1 1.22 0 1 3 2 1.26 0.36 0.04
##
## $Market.Cap
## vars n mean sd median trimmed mad min max
## X1 1 164 55229.49 40437.65 49254.58 51477.42 42578.32 2628.39 204103
## range skew kurtosis se
## X1 201474.6 0.89 0.8 3157.65
##
## $Turnover
## vars n mean sd median trimmed mad min max range skew kurtosis
## X1 1 164 68.88 58.26 51.5 58.64 33.36 2 369 367 2.17 5.82
## se
## X1 4.55
##
## $Net.Assets..Cr.
## vars n mean sd median trimmed mad min max range
## X1 1 164 4086.98 5136.47 2265.55 2928.68 2562.82 26.34 21621.14 21594.8
## skew kurtosis se
## X1 1.85 2.6 401.09
##
## $Standard.Deviation
## vars n mean sd median trimmed mad min max range skew kurtosis
## X1 1 164 14.92 1.97 14.73 14.81 1.99 10.66 21.07 10.41 0.54 0.13
## se
## X1 0.15
##
## $Sharpe.Ratio
## vars n mean sd median trimmed mad min max range skew kurtosis
## X1 1 164 0.74 0.2 0.72 0.73 0.18 0.35 1.33 0.98 0.52 0.02
## se
## X1 0.02
##
## $Sortino.Ratio
## vars n mean sd median trimmed mad min max range skew kurtosis
## X1 1 164 1.07 0.26 1.04 1.06 0.27 0.57 1.82 1.25 0.38 -0.51
## se
## X1 0.02
##
## $Beta
## vars n mean sd median trimmed mad min max range skew kurtosis
## X1 1 164 0.94 0.1 0.94 0.94 0.09 0.61 1.19 0.58 -0.12 0.72
## se
## X1 0.01
##
## $Alpha
## vars n mean sd median trimmed mad min max range skew kurtosis
## X1 1 164 6.74 3.65 6.47 6.43 3.39 0.52 19.09 18.57 0.78 0.58
## se
## X1 0.29
##
## $R.Squared
## vars n mean sd median trimmed mad min max range skew kurtosis
## X1 1 164 0.77 0.12 0.76 0.78 0.14 0.39 0.98 0.59 -0.48 -0.04
## se
## X1 0.01
##
## $Expense.Ratio
## vars n mean sd median trimmed mad min max range skew kurtosis
## X1 1 164 1.64 0.6 1.58 1.65 0.76 0.19 2.72 2.53 -0.05 -1.06
## se
## X1 0.05
##
## $Tenure.1
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 164 4.57 2.7 5.2 4.33 2.08 0.2 14.2 14 0.83 1.1 0.21
##
## $Tenure.2
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 164 0.81 1.72 0 0.36 0 0 10.8 10.8 2.89 9.73 0.13
##
## $Tenure3
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 164 0.16 0.56 0 0 0 0 3.2 3.2 3.3 9.78 0.04Box Plot
lapply(dfrModel, FUN=Graph_Boxplot)## $X1.Year.Return
##
## $Investment.Style
##
## $Market.Cap
##
## $Turnover
##
## $Net.Assets..Cr.
##
## $Standard.Deviation
##
## $Sharpe.Ratio
##
## $Sortino.Ratio
##
## $Beta
##
## $Alpha
##
## $R.Squared
##
## $Expense.Ratio
##
## $Tenure.1
##
## $Tenure.2
##
## $Tenure3
Observation
There are few outliers in the datasets
Tables
Investment_Style <- table(dfrModel$Investment.Style)
Investment_Style##
## 1 2 3
## 119 43 2prop.table(Investment_Style)##
## 1 2 3
## 0.72560976 0.26219512 0.01219512Observations
Here
1 Implies Growth Investment Style
2 Implies Blend Investment Style
3 implies Value Investment Style
Scatter Plot
plot(y=dfrModel$X1.Year.Return, x=dfrModel$Net.Assets..Cr.,
col="green",
ylim=c(0, 50), xlim=c(0, 22000),
main="Relationship Btw Return & Net Assets",
ylab="Return", xlab="Net Assets(Crs)")
scatterplot(dfrModel$X1.Year.Return, dfrModel$Standard.Deviation , main="Relationship Btw Risk & Return", xlab="Risk", ylab="Return")
plot((dfrModel$Investment.Style),jitter(dfrModel$X1.Year.Return),
col="green",
ylim=c(0, 30), xlim=c(1,3),
main="Relationship Btw Investment Style & Return",
ylab="Hotel Rent", xlab="Investment Style")
plot(y=dfrModel$X1.Year.Return, x=dfrModel$Sharpe.Ratio,
col="blue",
ylim=c(0, 40), xlim=c(0, 2),
main="Relationship Btw Room Rent and Star Rating of Hotel",
ylab="Hotel Rent", xlab="Star Rating")
scatterplot(dfrModel$X1.Year.Return, dfrModel$Sharpe.Ratio , main="Relationship Btw Sharpe Ratio & Return", xlab="Sharpe Ratio", ylab="Return")
plot(y=dfrModel$X1.Year.Return, x=dfrModel$Alpha,
col="green",
ylim=c(0, 40), xlim=c(0, 25),
main="Relationship Btw Alpha and Return",
ylab="Return", xlab="Alpha")
scatterplot(dfrModel$Alpha, dfrModel$X1.Year.Return , main="Relationship Btw Alpha & Return", xlab="Alpha", ylab="Return")
Observations
1.Above scatter plot is showing some relationship between Hotel rent and other Independent variables.
Correlation Plot
#pairs(dfrModel)
corrplot(corr=cor(dfrModel[ , c(1,2,3,4,5)], use="complete.obs"),
method ="ellipse")
corrplot(corr=cor(dfrModel[ , c(1,6,7,8,9)], use="complete.obs"),
method ="ellipse")
corrplot(corr=cor(dfrModel[ , c(1,10,11,12,12)], use="complete.obs"),
method ="ellipse")
Observations
1. We can see few variables are having very good correlation with Annual Return on different schemes 2. Standard Deviation, Sharpe Ratio & Alpha is very good coorelated with Annual Return.
Correlation Matrix
cor(dfrModel[, c(1:13)]) ## X1.Year.Return Investment.Style Market.Cap Turnover
## X1.Year.Return 1.00000000 -0.10456392 -0.47907689 0.04934270
## Investment.Style -0.10456392 1.00000000 -0.30875673 0.06848923
## Market.Cap -0.47907689 -0.30875673 1.00000000 0.14590358
## Turnover 0.04934270 0.06848923 0.14590358 1.00000000
## Net.Assets..Cr. -0.33953020 -0.02665225 0.16388213 -0.05273135
## Standard.Deviation 0.34830314 0.23074846 -0.45929001 -0.01605976
## Sharpe.Ratio 0.62250124 0.06508839 -0.65138337 -0.20646989
## Sortino.Ratio 0.49454982 0.18243604 -0.59703821 -0.18234403
## Beta 0.10313459 0.20756302 -0.13703157 0.02673520
## Alpha 0.65548490 0.11697120 -0.70263481 -0.16780872
## R.Squared -0.46001065 -0.16047660 0.76549672 0.18005088
## Expense.Ratio 0.00260838 -0.07250175 -0.05679234 -0.13159745
## Tenure.1 -0.12422889 -0.02376280 -0.08835882 -0.08106243
## Net.Assets..Cr. Standard.Deviation Sharpe.Ratio
## X1.Year.Return -0.33953020 0.34830314 0.62250124
## Investment.Style -0.02665225 0.23074846 0.06508839
## Market.Cap 0.16388213 -0.45929001 -0.65138337
## Turnover -0.05273135 -0.01605976 -0.20646989
## Net.Assets..Cr. 1.00000000 -0.20631606 -0.08135486
## Standard.Deviation -0.20631606 1.00000000 0.31568785
## Sharpe.Ratio -0.08135486 0.31568785 1.00000000
## Sortino.Ratio -0.03232574 0.32358763 0.93586004
## Beta -0.07889484 0.64339918 -0.08257479
## Alpha -0.12148716 0.54380060 0.95774922
## R.Squared 0.15267659 -0.40079552 -0.70905176
## Expense.Ratio -0.02186945 0.11909018 -0.06343242
## Tenure.1 0.23141599 -0.01427949 -0.04102574
## Sortino.Ratio Beta Alpha R.Squared
## X1.Year.Return 0.49454982 0.10313459 0.65548490 -0.46001065
## Investment.Style 0.18243604 0.20756302 0.11697120 -0.16047660
## Market.Cap -0.59703821 -0.13703157 -0.70263481 0.76549672
## Turnover -0.18234403 0.02673520 -0.16780872 0.18005088
## Net.Assets..Cr. -0.03232574 -0.07889484 -0.12148716 0.15267659
## Standard.Deviation 0.32358763 0.64339918 0.54380060 -0.40079552
## Sharpe.Ratio 0.93586004 -0.08257479 0.95774922 -0.70905176
## Sortino.Ratio 1.00000000 -0.08174135 0.90493003 -0.67118231
## Beta -0.08174135 1.00000000 0.07931381 0.12212232
## Alpha 0.90493003 0.07931381 1.00000000 -0.77474175
## R.Squared -0.67118231 0.12212232 -0.77474175 1.00000000
## Expense.Ratio -0.15805730 0.07806508 -0.01624840 -0.05942900
## Tenure.1 -0.03869664 0.08135126 -0.05055422 -0.02188434
## Expense.Ratio Tenure.1
## X1.Year.Return 0.00260838 -0.12422889
## Investment.Style -0.07250175 -0.02376280
## Market.Cap -0.05679234 -0.08835882
## Turnover -0.13159745 -0.08106243
## Net.Assets..Cr. -0.02186945 0.23141599
## Standard.Deviation 0.11909018 -0.01427949
## Sharpe.Ratio -0.06343242 -0.04102574
## Sortino.Ratio -0.15805730 -0.03869664
## Beta 0.07806508 0.08135126
## Alpha -0.01624840 -0.05055422
## R.Squared -0.05942900 -0.02188434
## Expense.Ratio 1.00000000 0.20736973
## Tenure.1 0.20736973 1.00000000Correlation with Room Rent
Correlation
vctCorr = numeric(0)
for (i in names(dfrModel)){
cor.result <- cor(dfrModel$X1.Year.Return, as.numeric(dfrModel[,i]))
vctCorr <- c(vctCorr, cor.result)
}
dfrCorr <- vctCorr
names(dfrCorr) <- names(dfrModel)
dfrCorr## X1.Year.Return Investment.Style Market.Cap
## 1.00000000 -0.10456392 -0.47907689
## Turnover Net.Assets..Cr. Standard.Deviation
## 0.04934270 -0.33953020 0.34830314
## Sharpe.Ratio Sortino.Ratio Beta
## 0.62250124 0.49454982 0.10313459
## Alpha R.Squared Expense.Ratio
## 0.65548490 -0.46001065 0.00260838
## Tenure.1 Tenure.2 Tenure3
## -0.12422889 -0.09932426 -0.13050913Visualize
dfrGraph <- gather(dfrModel, variable, value, -X1.Year.Return)
head(dfrGraph)## X1.Year.Return variable value
## 1 12.90 Investment.Style 1
## 2 14.35 Investment.Style 1
## 3 16.39 Investment.Style 2
## 4 14.86 Investment.Style 2
## 5 11.32 Investment.Style 1
## 6 12.67 Investment.Style 1ggplot(dfrGraph) +
geom_jitter(aes(value,X1.Year.Return, colour=variable)) +
geom_smooth(aes(value,X1.Year.Return, colour=variable), method=lm, se=FALSE) +
facet_wrap(~variable, scales="free_x") +
labs(title="Relation Of Return With Other Features")
Regression Analysis
Find Best Multi Linear Model for Economy Class
Choose the best linear model by using step(). Choose a model by AIC in a Stepwise Algorithm
In statistics, stepwise regression is a method of fitting regression models in which the choice of predictive variables is carried out by an automatic procedure. In each step, a variable is considered for addition to or subtraction from the set of explanatory variables based on some prespecified criterion.
The Akaike information criterion (AIC) is a measure of the relative quality of statistical models for a given set of data. Given a collection of models for the data, AIC estimates the quality of each model, relative to each of the other models. Hence, AIC provides a means for model selection.
#?step()
stpModel=step(lm(data=dfrModel, X1.Year.Return~.), trace=0, steps=1000)
stpSummary <- summary(stpModel)
stpSummary ##
## Call:
## lm(formula = X1.Year.Return ~ Investment.Style + Market.Cap +
## Turnover + Net.Assets..Cr. + Standard.Deviation + Sortino.Ratio +
## Alpha + R.Squared + Tenure.2, data = dfrModel)
##
## Residuals:
## Min 1Q Median 3Q Max
## -11.2545 -2.0996 -0.0058 2.3818 14.2957
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1.958e+01 5.260e+00 3.723 0.000276 ***
## Investment.Style -1.836e+00 7.250e-01 -2.532 0.012352 *
## Market.Cap -2.435e-05 1.275e-05 -1.910 0.058016 .
## Turnover 1.462e-02 5.310e-03 2.752 0.006629 **
## Net.Assets..Cr. -2.837e-04 6.030e-05 -4.705 5.60e-06 ***
## Standard.Deviation -6.011e-01 2.214e-01 -2.714 0.007398 **
## Sortino.Ratio -1.251e+01 3.364e+00 -3.719 0.000280 ***
## Alpha 2.206e+00 3.069e-01 7.188 2.66e-11 ***
## R.Squared 1.273e+01 4.774e+00 2.667 0.008465 **
## Tenure.2 -3.164e-01 1.750e-01 -1.808 0.072623 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 3.814 on 154 degrees of freedom
## Multiple R-squared: 0.6214, Adjusted R-squared: 0.5992
## F-statistic: 28.08 on 9 and 154 DF, p-value: < 2.2e-16Model1
## ------------------------------------------------------------------------
Model1 <- X1.Year.Return ~ Investment.Style+Market.Cap+Turnover+Net.Assets..Cr.+Standard.Deviation+Sharpe.Ratio+Sortino.Ratio+Beta+Alpha+R.Squared+Expense.Ratio+Tenure.1+Tenure.2+Tenure3
fit1 <- lm(Model1, data = dfrModel)
summary(fit1)##
## Call:
## lm(formula = Model1, data = dfrModel)
##
## Residuals:
## Min 1Q Median 3Q Max
## -11.1177 -2.0616 0.0876 2.1490 14.2157
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 2.431e+01 8.704e+00 2.793 0.005904 **
## Investment.Style -2.024e+00 7.491e-01 -2.702 0.007686 **
## Market.Cap -2.395e-05 1.409e-05 -1.700 0.091303 .
## Turnover 1.435e-02 5.612e-03 2.556 0.011590 *
## Net.Assets..Cr. -2.904e-04 6.392e-05 -4.543 1.14e-05 ***
## Standard.Deviation -1.030e+00 4.422e-01 -2.329 0.021190 *
## Sharpe.Ratio -9.364e+00 1.281e+01 -0.731 0.465914
## Sortino.Ratio -1.144e+01 3.861e+00 -2.962 0.003557 **
## Beta 5.323e+00 5.113e+00 1.041 0.299604
## Alpha 2.731e+00 7.863e-01 3.473 0.000675 ***
## R.Squared 1.216e+01 6.204e+00 1.960 0.051852 .
## Expense.Ratio -1.872e-01 5.614e-01 -0.333 0.739295
## Tenure.1 -8.054e-03 1.266e-01 -0.064 0.949359
## Tenure.2 -3.490e-01 2.017e-01 -1.730 0.085704 .
## Tenure3 2.965e-01 6.343e-01 0.467 0.640873
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 3.852 on 149 degrees of freedom
## Multiple R-squared: 0.6263, Adjusted R-squared: 0.5912
## F-statistic: 17.84 on 14 and 149 DF, p-value: < 2.2e-16Model Fit
## ------------------------------------------------------------------------
library(leaps)
leap1 <- regsubsets(Model1, data = dfrModel, nbest=1)
# summary(leap1)
plot(leap1, scale="adjr2")
Observations
The best fit model excludes Sharpe Ratio, Beta & Expense Ratio. Therefore, in our next model, we rerun the regression, excluding these variables.
Model2
## ------------------------------------------------------------------------
Model2 <- X1.Year.Return ~ Investment.Style + Market.Cap +
Turnover + Net.Assets..Cr. + Standard.Deviation + Sortino.Ratio +
Alpha + R.Squared + Tenure.2
fit2 <- lm(Model2, data = dfrModel)
summary(fit2)##
## Call:
## lm(formula = Model2, data = dfrModel)
##
## Residuals:
## Min 1Q Median 3Q Max
## -11.2545 -2.0996 -0.0058 2.3818 14.2957
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1.958e+01 5.260e+00 3.723 0.000276 ***
## Investment.Style -1.836e+00 7.250e-01 -2.532 0.012352 *
## Market.Cap -2.435e-05 1.275e-05 -1.910 0.058016 .
## Turnover 1.462e-02 5.310e-03 2.752 0.006629 **
## Net.Assets..Cr. -2.837e-04 6.030e-05 -4.705 5.60e-06 ***
## Standard.Deviation -6.011e-01 2.214e-01 -2.714 0.007398 **
## Sortino.Ratio -1.251e+01 3.364e+00 -3.719 0.000280 ***
## Alpha 2.206e+00 3.069e-01 7.188 2.66e-11 ***
## R.Squared 1.273e+01 4.774e+00 2.667 0.008465 **
## Tenure.2 -3.164e-01 1.750e-01 -1.808 0.072623 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 3.814 on 154 degrees of freedom
## Multiple R-squared: 0.6214, Adjusted R-squared: 0.5992
## F-statistic: 28.08 on 9 and 154 DF, p-value: < 2.2e-16Observations of Regression Analysis
Null Hypothesis
There is no dependency between return by mutual fund and other variables
Alternative Hypothesis
There is dependency between return by mutual fund and other variables
As per regression model we find out that P Value is less than 0.05 which means we are rejecting the NULL Hypothesis at 95% Confidence interval.
As well as we can see that F value is very high which means means of all the variables differ.
Below are the 9 variables which are affecting the price of the Room of the hotels, As well as they are in the order of significance to affect the return of Mutual Fund
Alpha
Sortino.Ratio
Net.Assets..Cr.
Standard.Deviation
R.Square
Turnover
Investment Style
Market Cap
Tenure
VISUALIZE THE BETA COEFFICIENTS AND THEIR CONFIDENCE INTERVALS FROM MODEL 2
library(coefplot)
coefplot(fit2, intercept= FALSE, outerCI=1.96,coefficients=c("Investment.Style","Market.Cap", "Net.Assets..Cr.", "Standard.Deviation", "Sortino.Ratio", "Alpha", "R.Squared", "Tenure.2"))## Warning: Ignoring unknown aesthetics: xmin, xmax
## ------------------------------------------------------------------------
# the Adjusted R Squared for Model 2 is less than Model 1
summary(fit1)$adj.r.squared## [1] 0.5911897summary(fit2)$adj.r.squared## [1] 0.5992417# the AIC for Model 2 is less than Model 1
AIC(fit1)## [1] 924.004AIC(fit2)## [1] 916.1546Observations
1. We can see that Adjusted R square value is more for model 2 instead of model 1 so model 2 is better
2. As well as AIC Value is less than Model 1, so Model 2 is better
Summary
- Data has been loaded successfully
- Data has been summarized to know the different statistical values
- Outliers has been find out in each variable and Evry variable is plotted on Box plot to know about the outliers
- Scatter plot as well as Corrgram is plotted which is showing the relationship between Room Rent and other variables
- Continuous variable are shown on Box plot while tables is used for discrete variables.
- For Regression Analysis,
Dependent Variable: Return by Mutual Fund Schemes
Below are the 9 variables which are affecting the return of Mutual Funds, As well as they are in the order of significance to affect the room rent
Alpha Sortino.Ratio
Net.Assets..Cr.
Standard.Deviation
R.Square
Turnover
Investment Style
Market Cap
Tenure
###########End of the Project#########