SPSS Exercise

SPSS Exercise

Comparing Means:

One or Two Samples t-Tests

t- Test and Z-Tests are commonly used for making comparisons between the means of the two samples or between some standard value and the mean of one sample.

One sample test:

A Business school in its advertisement claims that the average salary of its graduates in a particular lean year is at par with average salary offered at the top five business schools. A sample of 30 graduates from the business school, whose claim was to be verified, was taken at random. There salaries are given at random below:

Average salary in that year was 7, 50,000H0: There is no difference between the average salary of the business school perceived and the average salary of the top five business schools.

Syntax Analyse-Compare means-one sample test

Fill in test value as 750

Results:

One-Sample Statistics

NMeanStd. DeviationStd. Error Mean

salary of the graduate student30713.5081.2614.84

One-Sample Test

Test Value = 750

tdfSig. (2-tailed)Mean Difference95% Confidence Interval of the Difference

LowerUpper

salary of the graduate student-2.46029.020-36.50-66.84-6.16

Result: Since the p value has come out to be .020 therefore the difference is significant to reject the null hypothesis.

Paired t-test

1. A corporate training institution claimed that its training program can greatly enhance the efficiency of the call centre employees. A big call centre sent some of its employees for the training program. The efficiency was measured by the no. of deals closed by each employee in a one month period. Data was collected for a one month period before sending the employees for the training program. After the training program the data was again collected on the same program. After the training program, data was collected on the same employees for a month period.

Variables: Employee, before, after

H0: There is no significant diff between employees before and after the training program.

H1: There is sig diff between employees before and after the training program.

Syntax:

Analyse-Compare means-paired sample t-test

Paired Samples Statistics

MeanNStd. DeviationStd. Error Mean

Pair 1before40.70204.318.965

after44.15206.1581.377

Paired Samples Correlations

NCorrelationSig.

Pair 1before & after20.281.230

Paired Samples Test

Paired DifferencestdfSig. (2-tailed)

MeanStd. DeviationStd. Error Mean95% Confidence Interval of the Difference

LowerUpper

Pair 1before - after-3.4506.4521.443-6.470-.430-2.39119.027

Since the p value is coming out to be .027 which is very much lower than .05 so the null hypothesis is rejected at 5% level of significance.

Independent sample test:

A study was conducted to compare the efficiency of the workers of the two mines, one with private ownership and other with government ownership. The researcher was of the view that there is no significant difference between the efficiency of the two. The tonnage of the minerals mined by a worker in one shift was chosen as a criteria to access its efficiency. 20 workers from private mines and 24 from government owned mines were selected and there average output per shift was recorded.

H0: average output of the workers from mine 1 equals to that of workers from mine 2.

Ho: Average output of the workers from mine 1 differs to that of workers from mine 2.

The data obtained is given bin the excel sheet.

syntax: Analyse-compare means- independent sample t-test

There will be two options in the next dialogue box

1. Grouping variable.(mine will be the grouping variable)

2. Test variable.(Out put)

Define groups: The other option available there is define groups, click of this and feed 1 in the first blank and 2 in the second. Click on continue and ok.

output:

Group Statistics

MINENMeanStd. DeviationStd. Error Mean

12042.555.481.23

22439.042.79.57

Independent Samples Test

Levene's Test for Equality of Variances t-test for Equality of Means

FSig.tdfSig. (2-tailed)Mean DifferenceStd. Error Difference95% Confidence Interval of the Difference

LowerUpper

OUTPUTEqual variances assumed15.990.0002.74242.0093.511.28.936.09

Equal variances not assumed 2.59627.044.0153.511.35.746.28

In this table we get results of two tests- Levenes test for equality of variances and t-test for equality of means. The table contains two sets of analysis, the first one assuming equal variance in the two groups and the second one consisting unequal variances. The Levenes test tells us which statistic to consider to analyse the equality means. It test the null hypothesis that the two groups have equal variances. A small value of significance (.000) associated with Levenes test indicates that the two groups have unequal variances and the null hypothesis is rejected that the variances are equal. Therefore the statistics associated with equal variance is not assumed should be used fot t-test of equality of means.

The t-test p-value has come out to be 0.015 which indicate that null hypothesis is rejected at 5% level of sig. Where as the difference is not significant at 1% level of sig. so null hyp. is accepted.

Chi Square Test:To find out the significance of association between educational background of PGDBM students and their performance in terms of grade.

Both the variables are coded as code and grade code representing educational background and grade obtained. Educational background their codes are given below:Educational Background CodeB.com. 1

B.E.

2

B.Sc.

3

BBA.

4

B.A.

5

Grade codes are as follows:

Grade obtained:grade code

A

1

B

2

C

3

These two variables cross tabulated for twenty five observations. A cross Tabulation with a chi-square test is performed

Syntax: Analyse- Descriptive- cross tabs- select one variable in row and the other one in column.

Case I

Armands Pizza Parlors is a chain of Italian Food restaurants located in a five state area. The most successful locations for Armands have been near college campuses. The manager believes that the quarterly sales of these restaurants (denoted by Y) are related positively to the size of the student population (denoted by X); that is the restaurant near campuses with a large population tend to generate more sales than those located near campuses with a small population. Use regression analysis to predict the sale of restaurant for a given data of 16000 students.

Students population and quarterly sales data for 10 Armands Pizza Parlors

Case II

The owner of the pizza Corner Bangalore would like to build a regression model consisting of six factors to predict the sale of pizza. Data for the past 15 months on sales and six different factors were collected for the purpose.

Input:

Dependent variables:

Y=Monthly sales in rs.

Independent variables:

X1= No. of delivery boys.

X2=Cost of ad in Rs.

X3=No. of out lets

X4=Variety of pizzas

X5=Competitors activity index

X6=No. of existing customers (000)

Case II

This problem relates to the taste and quality of food served to 28 customers in TAJ Group of Hotels chain. The customers, who are basically from the higher income group (HIG), were asked to give their opinion about the quality/ taste of the four common non vegetarian dishes served to them. The analysis of the problem gives us the variance between the dependent variable (rating) and the independent variable (Non Veg Dishes)

The following are the common dishes served at the above hotels chain restaurant are coded as follows:

CODE:

DISH NAME

1Chicken platter

2Honey chicken

3Chicken spinach

4Tandoori Chiken

In This problem we have to consider the four different non vegetarian dishes that are being offered by TAJ Group of hotels chain. The hotel group wants to test, which of the above mentioned dishes is being preferred by the target customers, that is HIG Customers. At random these 28 respondents were asked for their preference on a scale of 10. (1-not liked at all and 10 is most preferred dish).

Factor analysisPrivate domestic airline operators raised their airfare in December 2000. Indian Airlines, the government-owned carrier, was the only domestic airline which did not follow suit. Information available (source-Economic Times) showed that people still prefer to fly Jet Airways. Our group set out to ascertain the reasons for the above preference, as according to Indian Airlines officials domestic flyers are price-conscious customers.

Our study consisted of 20 respondents who had recently flown with Jet Airways. They were asked to indicate on a seven point scale (1 = completely agree, 7 = completely disagree), their agreement or disagreement with the set of 10 statements relating to their perceptions and attributes of the airline.

The 10 statements were as follows:

1. They (Jet Airways) are always on time.

2. The seats are very comfortable.

3. I love the food they provide.

4. Their air-hostesses are very beautiful.

5. My boss/friend flies with the same airline.

6. The airlines have younger aircrafts.

7. I get the advantage of a frequent flyer program.

8. It (the flight timing) suits my schedule.

9. My mom feels safe when I fly Jet.

10. Flying Jet compliments my lifestyle and social standing in the society.

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