Presentation Assessment 2022

PRESENTATION
Student Name
Professor’s Name
University Name
Date

Average Rating by Television Network
 The average rating for all CBC movies was 13.433
 The average rating for all ABN movies was 15.268
 The average rating for all BBS movies was 14.405
The above result show that the ABN movies are the best performing because they have a higher average rating.
The followed by BBS movies with and average rating of 14.405. The least performing movies are CBC movies
with an average rating of 13.433
The table in below summarizes the data for the movie rating of the three television networks.

Descriptive Statistics
BBS ABN CBC
Mean 14.405 Mean 15.26842105 Mean 13.43333333
Standard Error 0.411254499 Standard Error 0.318203198 Standard Error 0.335806914
Median 14.4 Median 15.5 Median 12.85
Mode 15.6 Mode 12.8 Mode 12.4
Standard Deviation 1.839186033 Standard Deviation 1.961536252 Standard Deviation 1.839290218
Sample Variance 3.382605263 Sample Variance 3.847624467 Sample Variance 3.382988506
Kurtosis -0.382865613 Kurtosis -0.015379952 Kurtosis 2.103942049
Skewness -0.559459006 Skewness -0.2518147 Skewness 1.41496657
Range 6.3 Range 8.7 Range 7.8
Minimum 10.8 Minimum 10.8 Minimum 11.3
Maximum 17.1 Maximum 19.5 Maximum 19.1
Sum 288.1 Sum 580.2 Sum 403
Count 20 Count 38 Count 30
The table above shows the descriptive statistics developed using data analysis tool Pak.
 The standard deviation, standard error and the variance and the range show the dispersion or spread of the data.
 The median shows the rating that is at the middle of the distribution of each television network moving rating.
 The mode shows the most common moving rating.
 Maximum and minimum show the highest and lowest rating respectively.
 Sum and count show the sum of all rating and the count of the ratings respectively
 Skewness show the asymmetry in the data while kurtosis show how heavily the tails of a distribution differ from
the tails of a normal distribution

Line Graph with Fitted Linear Trend Line
The above plot indicates that there is a positive relationship between months and average monthly rating. The executives can
use the regression equation fitted in the plot to predict the average rating for the upcoming months. Y in the equation would
represent the dependent variable monthly rating while x would represent the independent variable month. If the regression
equation is used for forecasting, the r-square value shows that only 30.15% of the variation in monthly rating would be
explained by the month of rating thus we can expect the model to be 30.15% accurate.

Hypothesis Test
• The hypothesis test was conducted to determine whether there was a significant difference between the
ratings of movies with stars vs movies without stars so that the management can decide whether to hire stars
or not. The null and alternative hypothesis for the test were:
 Null hypothesis: There is no significant difference between the rating of movies with stars vs movies
without stars.
 Alternative hypothesis: There is a significant difference between the rating of movies with stars vs movies
without stars.
• The significance level for the test was 0.05 since we 95% confidence interval was used.
• The test conducted was a two tailed, two-sample t-test assuming equal variances.
• The test was conducted using the excel data analysis tool Pak and the results and analysis are shown below.

Hypothesis Test Results and Analysis
t-Test: Two-Sample Assuming Equal Variances
No stars With stars
Mean 13.52105263 13.28181818
Variance 4.333976608 1.969636364
Observations 19 11
Pooled Variance 3.489569378
Hypothesized Mean Difference 0
df 28
t Stat 0.338026163
P(T<=t) one-tail 0.368932438
t Critical one-tail 1.701130934
P(T<=t) two-tail 0.737864877
t Critical two-tail 2.048407142
From the results of above the p-value for the test is 0.73 and is greater than the significance level which is 0.05. Therefore, the
null hypothesis is not rejected. We conclude that there is sufficient amount of evidence to prove that there is no difference
between rating of movies with stars and without stars. A recommendation can therefore be given to the management not to
hire stars since they have no impact on moving rating.

Multiple Linear Regression
SUMMARY OUTPUT
Regression Statistics
Multiple R 0.266590902
R Square 0.071070709
Adjusted R Square 0.002261132
Standard Error 1.837209602
Observations 30
ANOVA
df SS MS F Significance F
Regression 2 6.972510373 3.486255187 1.032860717 0.369628859
Residual 27 91.13415629 3.375339122
Total 29 98.10666667
Coefficients Standard Error t Stat P-value Lower 95% Upper 95% Lower 95.0% Upper 95.0%
Intercept 13.00802213 0.559277 23.25863953 2.14855E-19 11.86048051 14.15556375 11.86048051 14.15556375
Fact 0.974757953 0.698469913 1.395561835 0.174218987 -0.458383929 2.407899835 -0.458383929 2.407899835
Stars 0.007952974 0.718241834 0.011072836 0.991246749 -1.465757539 1.481663487 -1.465757539 1.481663487
The multiple linear regression was performed to determine how moving rating is affect by the variable Fact and stars. The
dependent variable is rating while the independent variables are stars and fact. The output is shown below.

Analysis of Multiple Regression
a. The variable “Fact Based” has a higher impact on moving rating than “Having One Star.” The variable “Fact Based”
changes the moving rating by a magnitude of 0.975 while the variable “Having one Star” changes movie rating by a
magnitude of 0.00795.
b. Looking at the of significance F (0.3696) we can see it is larger than the significance level of 0.05, the model is not
statistically significant and therefore it does not explain the ratings very well and hence cannot be relied upon.
c. Neither of the independent variables are significantly related to the ratings at 95% confidence interval. This is because
the p values of both the independent variables are greater than the significance level of 0.05.

Thank You