In an effort to determine

if the whether or not new sales software is affecting the performance of salespeople

in four regions in which the company operates, the Northeast, Southeast,

Central, and West regions, I have created breakdown of the available sales data

since software implementation using the Chi-Square statistics and hypothesis

testing methods. The sales data I will be using reflects salespeople, totaling

500, who were divided in half, with half of them being provided with the sales

software and half of them not over the period of three months.

Part of this study is to

determine possible null and alternative hypotheses for a nonparametric test on

this data using chi-square distribution. To better describe these types of

hypotheses I refer to Dr. Tom Pierce of Raddford University (2010) who defines

them as “The null hypothesis is that the researcher’s prediction is not true.

The alternative hypothesis is that the researcher’s predicted difference is

true. So, the two-sample t-test gives us a way to decide between a null

hypothesis and an alternative hypothesis.” This study will use the

nonparametric qualitive data category of region. Elaborating, nonparametric data

is used on qualitive or categorical data such and gender, color, or the aforementioned

category of region.

The null hypothesis in

this study is that sales software does not make a difference in the performance

of salespeople in their duty to sell a certain number of products and therefore

that there is no statistical significance between the variable of having sales

software and an increase in sales. The alternative hypothesis in this study is

that there is a statistically significant relationship between the performance

of the sales people and whether or not they were provided with the new sales

software.

The chi-square test “is

intended to test how likely it is that an observed distribution is due to

chance. It is also called a “goodness of fit” statistic, because it

measures how well the observed distribution of data fits with the distribution

that is expected if the variables are independent.” (Dept. of Linguistics, Penn

State University, 2008) In this study I will use chi-square to analyze the

categorical data which has been provided.

Using the chi-square test

I have used the data to calculate the expected distribution. The estimated

value for each cell is the total for its row multiplied by the total for its

column, then divided by the total for the table: that is,

(RowTotal*ColTotal)/GridTotal (Dept. of Linguistics, Penn State University,

2008). The second table (below) now shows the distribution of the totals which

is based on the null hypothesis that the sales software will not have an impact

on the sales of salespeople in each region. In contrast you will see the first

table which reflects the actual data. The third table is the expected data. The

final table is the chi-square calculations.

Region

Software

No-Software

Totals

Actual Data

Northeast

165

100

265

Southeast

200

125

325

Central

175

125

300

West

180

130

310

Total

720

480

1200

Region

Software

No-Software

Totals

Null Hypothesis

Northeast

132.5

132.5

265

Southeast

162.5

162.5

325

Central

150

150

300

West

155

155

310

Total

600

600

1200

Region

Software

No-Software

Totals

Expected

Northeast

159.00

106.00

265.00

Southeast

195.00

130.00

325.00

Central

180.00

120.00

300.00

West

186.00

124.00

310.00

Total

720.00

480.00

1200.00

In order to complete the following table the cv and degrees of

freedom were calculated.

Observed

Expected

(O-E)

(O-E)2

(O-E)2/E

165

132.5

32.5

1056.25

7.97

200

162.5

37.5

1406.25

8.65

175

150

25

625

4.16

180

155

25

625

4.03

100

132.5

-32.5

1056.25

7.97

125

162.5

-37.5

1406.25

8.65

125

150

-25

625

4.16

130

155

-25

625

4.03

49.62

CV

7.814

( 3df and .05%

Based on the findings

here using the chi-square method, the null hypothesis must be rejected. The

observed chi-square of 49.62 is far higher than the 7.814 CV chi-square which

indicates that there is indeed a correlation between the amount of sales that a

salesperson makes and whether or not they are utilizing the new sales software

which also proves that we must accept the alternative hypothesis. As Stephanie

Glen (2013) of Statistics How To said, “A small chi-square value means that

there is very little relationship between your two variables. A larger value

means that there is a greater relationship between your two variables.”

Clearly, there is a strong relationship between our variable of use of sales

software and salesperson sales.