Q. Discuss the steps involved in drawing a graph in SPSS. How do you get
this graph to MS-Word/Excel ? (5 marks)
Here are the steps to create a graph in SPSS and export it to Word or Excel:
**Creating a Graph in SPSS:**
1. In SPSS, go to the menu at the top and navigate to Graphs >> Chart Builder
2. In the 'Choose From' list that appears, select the type of graph you want to create (e.g. Bar,
Scatter/Dot, Line, Histogram, etc.)
3. Select the specific graph subtype from the images provided and drag it to the panel at the top
4. From the Variables list on the left, select the variables you want to use for the graph and drag
them to the appropriate axis rectangles
5. Once you have set up the graph, click OK to generate it in the SPSS Viewer window
**Exporting the Graph to Word or Excel:**
1. In the SPSS Viewer window, right-click on the graph you just created and select Copy Graph
from the menu
2. Open your Word document or Excel spreadsheet where you want to insert the graph
3. In your document, use the Paste Special option and select Picture (Enhanced Metafile) to
paste the graph
4. The graph will be inserted as an image that you can resize and format as needed within your
Word or Excel file
By following these steps, you can easily create a variety of graphs in SPSS and export them to
Word or Excel documents for inclusion in reports, presentations, or further analysis.
Or
Here are the steps to create a graph in SPSS and export it to Word or Excel:
**Creating a Graph in SPSS:**
1. Go to Graphs >> Chart Builder
2. Choose graph type and subtype
3. Select variables for the graph
4. Click OK to generate the graph
**Exporting the Graph to Word or Excel:**
1. Right-click on the graph and select Copy Graph
2. Open Word/Excel and paste the graph using Paste Special > Picture (Enhanced Metafile)
Remember to modify graph properties in the Chart Editor if needed, and save the graph
separately if desired.
Sure, here is a detailed answer to each question from the provided paper:
**Unit-1**
**Q1(a). What is an operating system? What are its main purposes? Discuss any 3 types of
operating systems.**
An operating system (OS) is system software that manages computer hardware, software
resources, and provides common services for computer programs. Its main purposes are to
manage the computer's resources such as the CPU, memory, disk drives, and printers, establish
a user interface, and execute and provide services for software applications.
Three types of operating systems are:
1. **Batch Operating System**: Processes batches of jobs with minimal user interaction.
2. **Time-Sharing Operating System**: Allows multiple users to use the system interactively at
the same time.
3. **Distributed Operating System**: Manages a group of distinct computers and makes them
appear to be a single computer.
**Q1(b). Discuss any 5 descriptive measures of data. List any 5 MS Excel commands with
examples.**
Five descriptive measures of data are:
1. **Mean**: Average of data values.
2. **Median**: Middle value of data when sorted.
3. **Mode**: Most frequently occurring value.
4. **Standard Deviation**: Measure of the amount of variation or dispersion.
5. **Range**: Difference between the maximum and minimum values.
Five MS Excel commands with examples:
1. **AVERAGE(range)**: Calculates the mean of a group of numbers.
- Example: `=AVERAGE(A1:A10)`
2. **MEDIAN(range)**: Finds the median of a group of numbers.
- Example: `=MEDIAN(A1:A10)`
3. **MODE.SNGL(range)**: Determines the mode.
- Example: `=MODE.SNGL(A1:A10)`
4. **STDEV.P(range)**: Computes the standard deviation based on the entire population.
- Example: `=STDEV.P(A1:A10)`
5. **MAX(range)**: Returns the maximum value in a range.
- Example: `=MAX(A1:A10)`
**Q2(a). What are the different types of statistical data? Discuss various tabular and graphical
representations of data.**
Types of statistical data include:
1. **Nominal Data**: Categorical data without a specific order.
2. **Ordinal Data**: Categorical data with a specific order.
3. **Interval Data**: Numerical data without a true zero point.
4. **Ratio Data**: Numerical data with a true zero point.
Tabular representations include frequency tables and cross-tabulations. Graphical
representations include bar charts, histograms, pie charts, line graphs, and scatter plots.
**Q2(b). What do you understand by dispersion? Explain various measures of dispersion with
appropriate examples.**
Dispersion refers to the extent to which values in a dataset vary around the central tendency.
Measures of dispersion include:
1. **Range**: Difference between the maximum and minimum values.
- Example: If the dataset is [2, 3, 5, 8, 11], Range = 11 - 2 = 9.
2. **Variance**: Average squared deviation from the mean.
- Example: For the dataset [2, 4, 6], Mean = 4, Variance = [(2-4)² + (4-4)² + (6-4)²] / 3 = 2.67.
3. **Standard Deviation**: Square root of variance.
- Example: For the dataset [2, 4, 6], Standard Deviation = √2.67 ≈ 1.63.
4. **Interquartile Range (IQR)**: Difference between the first and third quartiles.
- Example: If Q1 = 25th percentile and Q3 = 75th percentile, IQR = Q3 - Q1.
**Unit-2**
**Q3(a). Give a detailed procedure on how to calculate mean, median, and mode in SPSS.**
To calculate the mean, median, and mode in SPSS:
1. Open SPSS and load the dataset.
2. Click on `Analyze` -> `Descriptive Statistics` -> `Frequencies`.
3. Select the variables for which you want to calculate the mean, median, and mode.
4. Click on the `Statistics` button.
5. Check `Mean`, `Median`, and `Mode`.
6. Click `Continue` and then `OK`.
7. The output window will display the mean, median, and mode.
**Q3(b). What is the purpose of analysis of variance (ANOVA) in statistics? Provide step-by-step
procedure on computing ANOVA in SPSS.**
ANOVA is used to compare the means of three or more samples to understand if at least one
sample mean is significantly different from the others. It helps determine if observed
differences are statistically significant.
To compute ANOVA in SPSS:
1. Open SPSS and load the dataset.
2. Click on `Analyze` -> `Compare Means` -> `One-Way ANOVA`.
3. Move the dependent variable to the `Dependent List` box.
4. Move the independent variable to the `Factor` box.
5. Click on `Options`, check `Descriptive`, and then `Continue`.
6. Click `OK` to run the analysis.
7. The output window will show ANOVA results including the F-value and significance level.
**Q4(a). What do you understand by skewness and how is it different from kurtosis? Discuss
various types of kurtosis found in the data.**
Skewness measures the asymmetry of the data distribution. Positive skewness indicates a long
tail on the right side, while negative skewness indicates a long tail on the left side. Kurtosis
measures the "tailedness" of the distribution.
Types of kurtosis:
1. **Leptokurtic**: Sharp peak and heavy tails (Kurtosis > 3).
2. **Mesokurtic**: Moderate peak and tails (Kurtosis ≈ 3, like a normal distribution).
3. **Platykurtic**: Flat peak and thin tails (Kurtosis < 3).
**Q4(b). Write down step-by-step procedure on how to compute mean deviation and standard
deviation of data in SPSS.**
To compute mean deviation and standard deviation in SPSS:
1. Open SPSS and load the dataset.
2. Click on `Analyze` -> `Descriptive Statistics` -> `Descriptives`.
3. Select the variables for which you want to calculate the mean and standard deviation.
4. Click on `Options`, check `Mean`, `Std. deviation`, and `Mean absolute deviation`.
5. Click `Continue` and then `OK`.
6. The output window will display the mean deviation and standard deviation.
**Unit-3**
**Q5(a). What is hypothesis testing and why is it required in statistics? Discuss any 2 such tests
with the help of examples and show how to perform any form of t-test in SPSS.**
Hypothesis testing is a statistical method used to make decisions about the population based on
sample data. It is required to determine the validity of assumptions or claims about a
population parameter.
Two tests:
1. **T-test**: Compares means between two groups.
- Example: Comparing test scores of two different classes.
2. **Chi-square test**: Tests the association between categorical variables.
- Example: Testing if gender is associated with political preference.
To perform a t-test in SPSS:
1. Open SPSS and load the dataset.
2. Click on `Analyze` -> `Compare Means` -> `Independent-Samples T Test`.
3. Move the test variable to the `Test Variable(s)` box.
4. Move the grouping variable to the `Grouping Variable` box and define the groups.
5. Click `OK`.
6. The output window will show the t-test results.
**Q5(b). Write short notes on simple, multiple, and dummy variable regression analysis.**
1. **Simple Regression**: Analyzes the relationship between two variables, one independent
and one dependent.
- Equation: \( Y = a + bX \)
2. **Multiple Regression**: Examines the relationship between one dependent variable and
multiple independent variables.
- Equation: \( Y = a + b1X1 + b2X2 + ... + bnXn \)
3. **Dummy Variable Regression**: Uses categorical variables by coding them as 0 or 1 to
include in regression analysis.
- Example: Gender coded as 0 (Male) and 1 (Female).
**Q6(a). What do you understand by simultaneous equations model estimation? Provide
step-by-step procedure in SPSS to estimate any one simultaneous equation model.**
Simultaneous equations model estimation involves modeling multiple interrelated equations
where the dependent variables in some equations can be the independent variables in others.
To estimate a simultaneous equations model in SPSS:
1. Open SPSS and load the dataset.
2. Click on `Analyze` -> `Regression` -> `Two-Stage Least Squares (2SLS)`.
3. Define the equations by specifying the dependent and independent variables.
4. Specify the instrumental variables if needed.
5. Click `OK`.
6. The output will show the estimation results for the simultaneous equations.
**Q6(b). List down the procedure to perform simple regression analysis of data in SPSS.**
To perform simple regression analysis in SPSS:
1. Open SPSS and load the dataset.
2. Click on `Analyze` -> `Regression` -> `Linear`.
3. Move the dependent variable to the `Dependent` box.
4. Move the independent variable to the `Independent(s)` box.
5. Click `OK`.
6. The output window will display the regression coefficients,
R-square value, and significance levels.
**Unit-4**
**Q7. Attempt any 3 parts from the following:**
**Q7(a). Write a short note on Disk Operating System and explain any 5 of its commands.**
Disk Operating System (DOS) is an operating system that runs from a hard disk drive. It was
widely used in the 1980s and early 1990s. DOS is command-line based and allows users to
manage files and execute programs.
Five commands:
1. **DIR**: Lists files and directories.
- Example: `DIR C:\`
2. **COPY**: Copies files from one location to another.
- Example: `COPY file1.txt D:\`
3. **DEL**: Deletes files.
- Example: `DEL file1.txt`
4. **MKDIR (MD)**: Creates a new directory.
- Example: `MKDIR newfolder`
5. **RENAME (REN)**: Renames files.
- Example: `RENAME oldfile.txt newfile.txt`
**Q7(b). How do we compute simple correlation between two variables in SPSS?**
To compute simple correlation in SPSS:
1. Open SPSS and load the dataset.
2. Click on `Analyze` -> `Correlate` -> `Bivariate`.
3. Select the two variables you want to correlate and move them to the `Variables` box.
4. Choose the correlation coefficient (Pearson by default).
5. Click `OK`.
6. The output window will display the correlation coefficient and significance level.
**Q7(c). Write a brief note on auto-regressive model.**
An auto-regressive (AR) model is a type of time series model that uses observations from
previous time steps as input to predict the value at the next time step. It assumes that past
values have an effect on the current value.
- **Equation**: \( Y_t = c + \phi_1 Y_{t-1} + \phi_2 Y_{t-2} + ... + \phi_p Y_{t-p} + \epsilon_t \)
where \( Y_t \) is the current value, \( c \) is a constant, \( \phi \) are parameters, and \(
\epsilon_t \) is the error term.
**Q7(d). Write a short note on various types of skewness found in data.**
Types of skewness:
1. **Positive Skewness**: Distribution with a longer tail on the right. Mean > Median > Mode.
2. **Negative Skewness**: Distribution with a longer tail on the left. Mean < Median < Mode.
3. **Zero Skewness**: Symmetrical distribution. Mean = Median = Mode.
**Q7(e). Explain von Neumann’s computer architecture with the help of a diagram.**
Von Neumann architecture is a computer design model that includes a processing unit, a control
unit, memory to store both data and instructions, external storage, and input/output
mechanisms. The architecture is based on the concept of a stored-program computer.
- Components:
1. **Central Processing Unit (CPU)**: Includes the Arithmetic Logic Unit (ALU) and control unit.
2. **Memory**: Stores data and instructions.
3. **Input/Output (I/O)**: Mechanisms for data exchange with the external environment.
- **Diagram**:
```
[Memory]
↑↓
[CPU] ↔ [I/O]
```
- Memory is connected to both the CPU and I/O devices, allowing the CPU to fetch and execute
instructions from memory and exchange data with I/O devices.
Correlation
Correlation is a statistical technique that can show whether and how strongly pairs of variables
are related.
The main result of a correlation is called the correlation coefficient (or “r”). It ranges from -1.0
to +1.0. The closer r is to +1 or -1, the more closely the two variables are related.
If r is close to 0, it means there is no relationship between the variables. If r is positive, it means
that as one variable gets larger the other gets larger. If r is negative it means that as one gets
larger, the other gets smaller (often called an “inverse” correlation).