Statistics_Computer assessment Essay Example
 Category:Business
 Document type:Assignment
 Level:Undergraduate
 Page:1
 Words:658
AFE135 Business Data Analysis
QUESTION 1

The survey method is using questionnaire. This is the most appropriate due to large population and various question which will be asked. The questionnaire will consist of both close ended and open ended questionnaire.

Simple random sampling is the most appropriate method of sampling technique to be used in this study. This is because it is simple and very much objective in dealing with large population

Two main variables is expenditure verses income

Reliability of the information which are being provided by the respondent may be in doubt since there is no way the researcher can authenticate the information.
QUESTION TWO

The recommended graphical comparison of each weekly takehome pay category should be histograms since they are more clearly giving the level of each frequency at every weekly takehome category. This is shown in the diagram below:
From the graph, it can be seen that group three has the highest frequency of 35 followed by group six with frequency of 30. This is followed closely by group four and two with 26 and 25 respectively group five is the second lowest with 19 frequency and the lowest is group one with 15.

Pie chart is the most appropriate in this case as it shows frequency and even the percentage of the proportion. This is shown in the figure below:
From the figure above, group 3 is having the highest propotion of 20% folloed closely by group 2 with 19%, group 4 having 18% while group five having 17%. Group 6 and group 1 are having 13% each and are the lowest.
Question three

To determine the number of classes, the following formula is most appropriate
To create grouping for characteristics with equal amplitude you just divide the range (max value min value) by the amplitude. if you have age between 10 and 90 yrs, if you want classes of 10 yrs amplitude (deciles) then the number of classes is : range/10=(9010)/10=8 classes.

Class width
The number of classes is what you want like in this case it is eight. From the data we have
Weekly take home pay
Highest = 1090
Lowest = 105
Class you need = 8
= (1090105)/8
Highest = 375
Lowest = 44
Class you need = 8
= (37344)/8

Histograms
From the two graphs, take home pay is not normally distributed but it tends to move to left, that is , it is left handed distributed while weekly expenditure is normally distributed to the center.
Question four

descriptive statistics
Takehome pay 
Weekly food expenditure 

501.5866667 
197.991266 

Standard Error 
19.40476241 
Standard Error 
6.756779581 
190.87045 

Standard Deviation 
237.6588324 
Standard Deviation 
82.75331139 
Sample Variance 
56481.72063 
Sample Variance 
6848.110547 
Kurtosis 
0.777016935 
Kurtosis 
0.909166653 
Skewness 
0.32628762 
Skewness 
0.088943042 
329.1642 

373.4779 

29698.6899 

Largest(1) 
Largest(1) 
373.4779 

Smallest(1) 
Smallest(1) 

Confidence Level (95.0%) 
38.34406623 
Confidence Level (95.0%) 
13.35148549 

Correlation coefficient
Takehome pay 
Weekly food expenditure 

Takehome pay 

Weekly food expenditure 
0.994480486 
The correlation coefficient is positively correlated and is new 1 or 1 hence there are perfectly positive correlation
Question five

From the analysis, food expenditure depends on weekly pay hence food expenditure is the dependent variable while weekly pay is the independent variable in this case

Line graph

Regression output
Regression Statistics 

Multiple R 
0.994480486 

0.988991437 

Adjusted R Square 
0.988917054 

Standard Error 
8.711902022 

Observations 

Significance F 

Regression 
1009135.68 
1009135.68 
13296.08 
8.1E147 

Residual 
11232.79105 
75.89723684 

1020368.471 

Coefficients 
Standard Error 
Lower 95% 

Intercept 
24.30172358 
1.665810737 
14.5885262 
1.94E30 
21.00988 
Takehome pay 
0.346280222 
0.003003073 
115.3086263 
8.1E147 
0.340346 
From the coefficient table above, the linear equation will be given by
y = 0.3463x + 24.302
The coefficient indicates that it is positively related and a unit increase in weekly pay increases food expenditure by 0.3463 while the intercept where even if there is no monthly income food expenditure will still be 24.302.

Correlation coefficient (R^{2})
From the table, the correlation coefficient is given as 0.988 indicating that 98.8% of the dependent variables is explained by independent variables in the model.