# Business Data Analysis Essay Example

• Category:
Statistics
• Document type:
Assignment
• Level:
• Page:
1
• Words:
484

COMPUTER APPLICATION

Question one

a) The method of survey the researcher will use is an empirical survey since there are many factors to be considered such as age, education levels, income levels and gender.

b) The sampling method that the researcher will employ is simple random sampling because the sample contains members with equal opportunities to be chosen for the research.

c) The two main variables the researcher should consider are; income and age since spending patterns tend to differ with age and the income levels. The type of data for the above variables is quantitative in nature hence it can be measured numerically.

d) i)Lack of resources to carry out the research.

ii)Lack of openness from the interviewee, this could be because not everyone is iii)comfortable to share with strangers how much they earn and how they spend their earnings.

iv)Dishonesty; the interviewee may feel uncomfortable to share especially if they are low income earners.

Question two

According to Ziegel, Eric R., and Michael Middleton, there are various methods of analysing data but for this study, the researcher should use a bar chart because presentation of data in a bar chart is easy can be retrieved easily.

The above chart represents both the weekly pay and expenditure and their frequencies respectively. From this chart, we can extract both weekly pay and weekly expenditure as shown in the following bar graphs;

Weekly pay;

The above bar chart represents the weekly pay. It is a graph of groups against frequency.

Question three

 Frequency 0 0 0 0 0 0
 Frequency 0 0 0 0 0 0
 Question four Take-home pay (x) Weekly expenditure (y) x=39300∑ y=29699∑ x²=46150179∑ y²=6890040∑ xy=17649903∑

Weekly take-home pay

Mean=∑x/n

=39300/150

Smallest and largest values

The smallest value in weekly home pay is and the largest is while in the weekly expenditure, the smallest value is 50 while the largest is 356.

Weekly food expenditure

=29699/150

B) CORRELATION COEFFICIENT (r)

r= { (n∑xy-∑x∑y)}

√{n∑x²-(∑x)²}*{n∑y²-(∑y)²}

(150*17649903) – (39300*29699)

√{150*46150179 – (39300)²}*{150*6890040 – (29699)²

1480314750

√{5378036850*151475399

=1480314750/902574250

1.6401-1

There is a positive relationship between the two variables.

QUESTION FIVE

a) The dependent variable is expenditure while the weekly home pay is the independent variable because expenditure depends on the weekly home pay.

Liner regression model

a=y*-bx*

y* and x* represents the means of y and x respectively

b= {n∑xy-∑x*∑y}/n∑x²-(∑x)²

{150*17649903-39300*29699}/150*46150179-(39300)²

1480314750/5378036850

b=0.2753

a= (39300/150)-0.2753*29699/150

=262 — 54.5076

a=207.4924

Therefore, Y=207.4924 ⁺ 0.2753x

A unit change in the weekly home pay will lead to a 0.2453 increase in the weekly expenditure.

D) Coefficient of determination (r²)

r=0.6401 or 64.01%

r²= 0.6401²

r²=0.4097 or 40.97%

This means that 40.97% changes in Y are contributed by changes in X and the remaining 59.02 are contributed by other factors that are not in the model.

REFERENCES

Ziegel, Eric R., and Michael Middleton. «Data Analysis Using Microsoft Excel 5.0.» Technometrics, vol 38, no. 1, 1996, p. 78. JSTOR, doi:10.2307/1268909.