Below are Tables 14A to 14D, summarising the results of data analyses of research
conducted in a sales organisation that operates in 50 different cities of the
country, with a total sales force of about 500.
The number of salesmen sampled for the study was 150.
Question
You are to:
You are to:
- Interpret the information contained in each of the tables, in as much detail as possible.
- Summarise the results for the CEO of the company.
- Make recommendations based on your interpretation of the results.
Table 14A
Means,
Standard Deviations, Minimum, and Maximum
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Std.
Variable Mean Deviation Minimum Maximum
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Sales (in $000s) 75.1 8.6 45.2 97.3
No. of salespeople 25 6 5 50
Population (in 000s)
5.1 0.8 2.78 7.12
Per capita income (in 000s 20.3 20.1 10.1 75.9
Advertising (in $000s) 10.3 5.2 6.1 15.7
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Answer
The average population of
the three cities is 5100 and the mean per capita income of the people is $
20,300. There is a wide variation in the income (std. dev. = 20.1), indicating
that some are quite rich while others are poor. For an average advertisement
expenditure of $10,300, the mean sales of $ 75,100 gets about $7.5 for each dollar expended on
advertisement.
Correlations Among the Variables
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Sales Salesmen Population Income Advertisement
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Sales 1.0
No. of salesmen
.76 1.0
Population
.62 .06 1.0
Income .56
.21 .11 1.0
Ad. Expenditure
.68 .16 .36 .23 1.0
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All
figures above .15 are significant at p -
.05.
All figures above .35 are
significant at p < .001.
Answer
Sales,
obviously the dependent variable, is highly significantly positively correlated
to the number of salespersons, the population, the income of the people, and
the advertisement dollars expended. This makes good sense. High income areas
have more salespersons who cover the territory to sell the product, More
advertising is aimed at the high income groups, and the more populated areas,
which seem to be good strategies for increasing sales and income for the
company.
In other words, sales are
significantly correlated at p<.001 level with all four of the variables in
the study. The number of salesmen and the advertisement expenditure are
understandably related to sales (r=.76 and .68, respectively) .
There are four other significant correlations, but
they may not be of much relevance to our interpretation of sales data.
Table
14C
Results of Oneway ANOVA: Sales by Level of Education
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Source
of Sums of Mean Significance
Variation Squares Df Square
F of
F
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Between Groups
50.7 2 12.7 3.6 .05
Within Groups
501.8 147 3.5
Total 552.5 149
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Answer
The
level of education of the salespersons does have an influence on the sales they
make, since the F value is significant at the .05 level. If we are to detect
where the differences lie, the Duncan
Multiple Range
test can be performed.
To put it differently, the one-way ANOVA table
indicates that there is a significant difference in the sales made by the three
groups of salesmen with different educational levels (F=3.6, p=.05). Further
tests such as the Duncan
Multiple Range
or the Bonferroni test will have to be conducted to know which of the groups
are significantly different.
Table 14D
Results
of Regression Analysis
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Multiple R .65924
R square .43459
Adjusted R square .35225
Standard error .41173
DF
(5,144)
F 5.278
Sig .000
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Variable BETA t
Sig t
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Training of salesmen .28 2.768 .000
No. of salesmen
.34 3.55
.000
Population
.09 0.97
.467
Per capita income .12 1.20
.089
Advertisement .47 4.54 .000
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Answer
The
five independent variables, together, explain 43 percent of the variance in
Sales. Advertisement seems to have the greatest influence on sales, judged by
the Beta weight of .47 (which is the highest), followed by the number of
salespersons (Beta of .34). The training of the salespersons also has a
significant influence on sales (Beta of .28). The population and per capita
income do not individually explain the variance in sales volume significantly.
So, if sales are to be increased, spending more advertising dollars, increasing
the number of salespersons, and offering good training to them, would help.
Summary of Results for CEO
The
sales made by different salespersons vary considerably, depending on many
factors. There is also considerable variation in the number of salespersons,
the population, and the per capita income of the people in the various areas.
The
level of education of the salespersons makes a difference to the sales volume
generated by the individuals. Further analysis can be done to see the best
level of education for generating more sales.
Advertisements
seem to boost sales considerably. The greater the number of salespersons
deployed, the more the sales. Better trained salespersons generate more sales
than those who are not as well trained. Neither the per capita income nor the
population size, seems to make a difference to the sales volume when all the
variables are considered simultaneously.
Recommendation
If the goal of the company is to increase the volume
of sales, spend more money on advertising, increase the number of salespersons,
and train them better. Recruiting salespersons who have the optimum level of
education will also help.
