UNIT 3 – SPSS for Data Analysis

2-Mark Questions:

1. What is data entry in SPSS?

Data entry in SPSS involves inputting raw data into the Data View window.
Each row represents a case or observation, and each column represents a
variable.

2. Mention any two tools used for data analysis in SPSS.

Descriptive Statistics

Correlation Analysis

3. What is meant by regression analysis?

Regression analysis is a statistical method used to examine the relationship

between a dependent variable and one or more independent variables.

5-Mark Questions:

1. Explain the procedure for calculating descriptive statistics in SPSS.

Open your dataset in SPSS.

Click on “Analyze” > “Descriptive Statistics” > “Frequencies” or

“Descriptives”.

Select the variables for which you want the statistics.

Choose the required statistics (mean, median, mode, etc.).

Click OK to generate the output.

2. How do you perform correlation analysis in SPSS?

Go to “Analyze” > “Correlate” > “Bivariate”.

Select the variables to correlate.

Choose the correlation coefficient (Pearson, Spearman, or Kendall).

Click OK to view results in the output window.

3. Write a short note on regression model in SPSS.

A regression model in SPSS is used to predict the value of a dependent

variable based on the value of one or more independent variables. SPSS
provides a regression function under “Analyze” > “Regression” > “Linear”.

10-Mark Questions:

1. Explain the steps to perform correlation and regression analysis in

SPSS.

Correlation Analysis:

Click “Analyze” > “Correlate” > “Bivariate”.

Choose variables, select Pearson or Spearman, and click OK.

Regression Analysis:

Go to “Analyze” > “Regression” > “Linear”.

Assign the dependent and independent variables.

Click OK to see the regression coefficients and model summary.

2. How do you forecast using regression models in SPSS? Explain with an

example.

Forecasting with regression involves using the regression equation to predict

values. For example, to forecast sales based on advertising budget:

Perform linear regression (sales = dependent, ad budget = independent).

Use the regression equation (Y = a + bX) to predict future sales for given ad
budgets.

SPSS can generate predicted values under “Save” options in regression

dialog.

3. Describe the process of data analysis using SPSS tools in detail.

Import or enter data.

Clean and define variables.

Perform descriptive analysis.

Conduct inferential statistics (t-test, ANOVA, regression).

Interpret output tables and charts.

Export results for reporting.

UNIT 4 – Statistical Inference

2-Mark Questions:

1. What is standard error?

Standard error measures the accuracy with which a sample represents a

population. It is the standard deviation of the sampling distribution.

2. What is sampling?

Sampling is the process of selecting a subset of individuals from a population

to estimate characteristics of the whole population.

3. What is a paired sample t-test?

A paired sample t-test compares means from the same group at different
times or under two different conditions.
5-Mark Questions:

1. Explain the central limit theorem.

The central limit theorem states that the distribution of sample means
approaches a normal distribution as the sample size becomes larger,
regardless of the population’s distribution.

2. What is the difference between small sample and large sample tests?

Small sample tests (n < 30): Use t-distribution.

Large sample tests (n ≥ 30): Use z-distribution.

Small samples require more assumptions and are less reliable.

3. Describe the procedure for testing means using t-test.

State the hypothesis.

Choose the appropriate t-test (one sample, paired, or independent).

Set significance level.

Calculate t-value using SPSS.

Interpret the results from the output.

10-Mark Questions:

1. Explain the steps in conducting large and small sample tests.

Define hypothesis.

Determine sample size.

For large samples: use z-test.

For small samples: use t-test.

Analyze using SPSS or manual formulas.

Compare p-value with significance level to make a decision.

2. Describe in detail the various types of sampling methods.

Simple Random Sampling: Equal chance for each member.

Systematic Sampling: Every k-th element is selected.

Stratified Sampling: Population divided into strata, then randomly sampled.

Cluster Sampling: Entire clusters are randomly selected.

Convenience Sampling: Non-random, based on ease of access.

3. How are mean, proportion, and paired observations tested using SPSS?

Mean: Use t-tests under “Analyze” > “Compare Means”.

Proportion: Use non-parametric tests or Chi-square.

Paired Observations: Use “Paired Samples T-Test” in SPSS.

UNIT 5 – Non-Parametric Tests & ANOVA

2-Mark Questions:

1. What is a non-parametric test?

A non-parametric test does not assume a specific distribution and is used for
ordinal data or non-normal distributions.

2. What is a one-way ANOVA?

One-way ANOVA is used to compare means of three or more groups based on

one independent variable.

3. What is the purpose of the Chi-square test?

It tests the association or independence between categorical variables.

5-Mark Questions:

1. Differentiate between one-way and two-way Chi-square test.

One-way Chi-square: Tests goodness of fit (observed vs. expected

frequency).

Two-way Chi-square: Tests independence between two variables in a

contingency table.

2. Explain the steps to perform a Chi-square test in SPSS.

Click “Analyze” > “Descriptive Statistics” > “Crosstabs”.

Select row and column variables.

Click “Statistics” > select “Chi-square”.

Click OK to get results.

3. Write short notes on ANOVA and its types.

ANOVA tests differences between group means.

One-way ANOVA: One independent variable.

Two-way ANOVA: Two independent variables.

MANOVA: Multiple dependent variables.

10-Mark Questions:

1. Explain the process of performing One-way and Two-way ANOVA in

SPSS.

One-way ANOVA:

Click “Analyze” > “Compare Means” > “One-Way ANOVA”.

Assign dependent and factor variables.

Click OK for results.

Two-way ANOVA:

Click “Analyze” > “General Linear Model” > “Univariate”.

Add dependent and fixed factors.

Click OK to interpret the interaction effects.

2. Describe the procedure for conducting Chi-square tests with examples.

Create a contingency table (e.g., Gender vs. Product Preference).

Go to “Analyze” > “Descriptive Statistics” > “Crosstabs”.

Select variables, click on “Statistics” > “Chi-square”.

SPSS gives Chi-square value and p-value. Interpret to determine

independence.

3. Discuss the importance of non-parametric tests in business research.

Non-parametric tests are essential when data doesn’t meet parametric

assumptions. They are useful for ordinal data, small samples, or skewed
distributions. Common in customer satisfaction, employee ranking, etc.