ECON1030 Business Statistics Assignment Help

ECON1030 Business Statistics Assignment Help

ECON1030 Business Statistics Assignment Help

A. Ans

Descriptive statistics of Gross Revenue:

Column1

 
  

Mean

65.92714286

Standard Error

12.74835205

Median

26.82

Mode

#N/A

Standard Deviation

111.8663352

Sample Variance

12514.07696

Kurtosis

11.76090225

Skewness

3.24359001

Range

651.6

Minimum

0.67

Maximum

652.27

Sum

5076.39

Count

77

This analysis tells that average gross revenue is 65.92. the standard deviation of this distribution is large so we can say that there is more fluctuation in the data.

Descriptive statistics of Theatres:

Column1

  

Mean

2460.052

Standard Error

117.8053

Median

2720

Mode

3003

Standard Deviation

1033.737

Sample Variance

1068613

Kurtosis

-0.87365

Skewness

-0.23422

Range

3658

Minimum

653

Maximum

4311

Sum

189424

Count

77

The data of theatres shows that the average value is 3003 and the standard deviation is 1033. It is negatively skewed data from skewness measure which is -0.234.

Descriptive statistics of MeteoriticScore:                               

Column1

  

Mean

54.71429

Standard Error

2.049445

Median

56

Mode

81

Standard Deviation

17.98381

Sample Variance

323.4173

Kurtosis

-0.99233

Skewness

0.061818

Range

67

Minimum

22

Maximum

89

Sum

4213

Count

77

The mean of a meteoritic score is 54.71. it is positively skewed data. Only 16 movies are of the category in a robot. Their average metacritique ranking is 54.66667 which shows that it is very close to the on an average metacritique ranking.

B. Ans

C. Ans

Box-and-whisker plot for the distribution of the gross revenue:

D. Ans

Correlation between a rating of a film and the revenue

0.160167647

They are positively correlated so we can say that both the data is in the same direction. If one is increasing then another will also increase.

E. Ans

Column1

  

Largest(1)

4311

Smallest(1)

653

Confidence Level(90.0%)

196.16365

F. Ans

No of action films analyses: = 13

Mean of their gross income = 228.38  millions

P value for the liklyhood test = 0.003102892 . so it is very less probable that film earn this much amount.

G. Ans

Regression output of Y

SUMMARY OUTPUT

       
         

Regression Statistics

       

Multiple R

0.781191

       

R Square

0.61026

       

Adjusted R Square

0.555433

       

Standard Error

58.77616

       

Observations

76

       
         

ANOVA

        

 

df

SS

MS

F

Significance F

   

Regression

8

367832.9

45979.11

15.21073

1.81E-12

   

Residual

68

234915.3

3454.637

     

Total

76

602748.2

 

 

 

   
         

 

Coefficients

Standard Error

t Stat

P-value

Lower 95%

Upper 95%

Lower 95.0%

Upper 95.0%

Intercept

-159.867

32.93921

-4.85339

7.42E-06

-225.596

-94.1376

-225.596

-94.1376

4291

0.047216

0.00852

5.541532

5.27E-07

0.030214

0.064218

0.030214

0.064218

59

1.354561

0.418749

3.234777

0.001881

0.518959

2.190162

0.518959

2.190162

1

65.2928

20.40998

3.199063

0.002095

24.56532

106.0203

24.56532

106.0203

1

46.23357

26.21074

1.763917

0.082238

-6.06914

98.53627

-6.06914

98.53627

0

13.34658

31.99987

0.417082

0.677932

-50.5082

77.20133

-50.5082

77.20133

0

12.27422

25.26018

0.485912

0.628592

-38.1317

62.68012

-38.1317

62.68012

0

13.32961

23.82147

0.559563

0.577616

-34.2054

60.86461

-34.2054

60.86461

0

0

0

65535

#NUM!

0

0

0

0

         
         
         

H. Ans

The regression line found In the previous examples tells the relationship between Y Gross Revenue in Millions of US$ and X Number of theatres. P value in this test is equal to 5.71E-11. This less than 0.05 so we can say that we need to accept the null hypothesis. The intercept of the coefficient is -93.6278 and the standard error in this 75.2322.

I. Ans

The regression line in above case can be written as

Y =  -159.867 + 0.047216 X1 + 1.354561 X2+  65.2928 X3 + 46.23357 X4  +  13.34658 X5  +  12.27422 X6 + 13.32961 X7 + 0 X8

It is a relationship between Gross Revenue in Millions of US$ and other variables. The gross revenue can be predicted from the number of theaters and other variables. The intercept part is -159.867. it is negative so we can say that in the case where all variables are 0 then it shows the negative gross income so there is a loss in this business. The coefficient of last variable is 0 , it means that it does not affect the gross income.

J. Ans

                                                    R Square

0.61026

                                                   Adjusted R Square

0.555433

The adjusted R squire is a modified version of R squire. In the prediction of the output we modified the relation and it shows that the updated r squire. If the adjusted R squire is larger than it means that the expected value is larger than the actual one.

K. Ans

Since p value is less than 0.05 in all cases except X3, X4, X5 so we can say that overall modal satisfying the required significance except these variables.

L. Ans

There are eight independent variables which effect the gross revenue in this regression modal but since the coefficient of X8 is 0 so we can say that except thriller all other factors effects the gross profit.

M. Ans

It is given that

Y gross profit

X1 Metacritique score of 30
X2    Shows in 4,000 theatres
X3  Not a sequel or a reboot

Regression analysis result

SUMMARY OUTPUT

       
         

Regression Statistics

       

Multiple R

0.767026

       

R Square

0.588329

       

Adjusted R Square

0.571176

       

Standard Error

58.70521

       

Observations

76

       
         

ANOVA

        

 

df

SS

MS

F

Significance F

   

Regression

3

354614.5

118204.8

34.29904

7.04E-14

   

Residual

72

248133.7

3446.302

     

Total

75

602748.2

 

 

 

   
         

 

Coefficients

Standard Error

t Stat

P-value

Lower 95%

Upper 95%

Lower 95.0%

Upper 95.0%

Intercept

-158.8

30.8676

-5.14457

2.23E-06

-220.334

-97.267

-220.334

-97.267

4291

0.051576

0.007616

6.771887

2.9E-09

0.036393

0.066759

0.036393

0.066759

59

1.447145

0.385132

3.757532

0.000346

0.679399

2.214892

0.679399

2.214892

1

66.64747

19.42855

3.430388

0.001001

27.91736

105.3776

27.91736

105.3776

         
         
         

Regression line is Y = -158.8 + 0.051 X1+1.447 X2+ 66.64 X3

Predicted Y =-158.8 + 0.051 x 30+1.447 x 4000+ 66.64 x 0

Y = 5601.65

N. Ans

Following graph shows the connection among these three variables. The curves shows that the nonlinear distribution of Gross profit, the theatres. The graph of metacritique shows the linear distribution or we can say that it is closed to linear distribution.

O. Ans

The above analysis tells very much information about the movies interest Australia. We can see that only 16 movies are of category reboot. On other side 15 movies are of comedy category their average metacritique ranking is 51.66667.  it is very less in the case of metacritique ranking of the movies which are under reboot category. So it directly tells that most of the people taking interest in the movies of reboot category. This interest is larger than the movies of action categories. There are 14 movies are of categories in action so we can see that the average of the metacritique ranking of these movies is 52.71429. it is less than the average ranking.  The average gross income of action movies is 156.37. it is larger than the average income of all type of the movies so we can try to analyze this interest also.