Relationship between Annual Income and Restaurant Expenditure - Example

The paper "Relationship between Annual Income and Restaurant Expenditure" is a perfect example of a finance and accounting report. This report is the result of an investigation on the customer characteristics who are likely to be customers in a fine, upscale restaurant with fine entrées, drinks, and desserts in a serene atmosphere. The study is done with full knowledge in restaurant operations and some knowledge in upscale restaurants but there is no surety that the city may require the need for such a restaurant. The major interest is establishing if there will be demand for the restaurant and the appropriate pricing. In any market, there is likely to be a big difference in markets with regards to what consumer prefers and there income level (Daellenbach, 1994).

The hypothesis of the study is

There is a significant correlation between the income and what one spends in restaurants

Methodology

In this study, 50 respondents were randomly approached and were requested to fill in the questionnaire regarding the possibility of a restaurant being established in the area of investigation.

1. Obtaining two sets of 50 raw data pertaining to some business units.

The respondents were asked to give the amount in dollars they spend on food and beverages in one sitting in a restaurant. They were also asked to state their annual income in dollars.

2 Frequency distribution and histogram for the data using 7or 8 classes.

Expenditure classes

Class 1

<$2

Class2

$2-$4

Class3

$4.01-$6

Class4

$6.01-$8

Class5

$8.01-$10

Class6

$10.01-$12

Class 7

>$12

Annual income classes

Class 1

<$10000

Class2

$10000-$19999

Class3

$20000-$29999

Class4

$30000-$39999

Class5

$40000-$49999

Class6

$50000-$59999

Class 7

$60000-$69999

Class 8

>$70000

Figure 1

Figure 2

3. Observation from the histogram about the data?

From the histogram, it can be seen that the income data is much closer to normal distribution while the expenditure data is slightly skewed.

4. Using the data to find the descriptive measures of the data.

Table 1

Column1

Mean

3.24

Standard Error

0.238293251

Median

3

Mode

2

Standard Deviation

1.684987737

Sample Variance

2.839183673

Kurtosis

0.731597038

Skewness

1.418165644

Range

5

Minimum

2

Maximum

7

Sum

162

Count

50