Using the survey questionnaire and the 30 completed surveys attached:

1. You will be creating a Codebook and input all the Data in the EXCEL FILE.

2. Conduct Descriptive Statistics (Mean, Median, Mode, Range, etc.) –> Make sure to do descriptive statistics for at least 4 different questions (you can use same descriptive stat. for more than 1 question).

3. Conduct Inferential Statistics –> Conduct at least 1 of each Chi-Square, T-test, and Correlation and write down the interpretation.

4. Create a visualization –> Conduct at least 1 table and 3 figures

Do all 4 tasks and submit Excel and/or Word file(s) based on your preference.

Struggling with where to start this assignment? Follow this guide to tackle your assignment easily!

To complete your data analysis assignment, we will go step-by-step through each task. The following guide will walk you through the necessary steps to ensure a clear, organized, and accurate approach.

Task 1: Create a Codebook and Input Data into the Excel File

A Codebook is essential for explaining each variable in your survey and the way they are measured. It will serve as a reference to understand the data entered.

Create the Codebook in Excel:

In your Excel file, add a new sheet titled “Codebook.”
For each variable/question in your survey, list:
- Variable Name: A brief identifier for the question (e.g., “Age”, “Income”, “Satisfaction Level”).
- Variable Description: A description of what the variable represents.
- Values/Response Codes: What the responses mean (e.g., 1 = Male, 2 = Female for gender).
- Measurement Scale: Specify if the variable is nominal, ordinal, interval, or ratio.

Example:

Variable Name	Variable Description	Response Codes	Measurement Scale
Age	Respondent’s age	18-25, 26-35, 36-45	Ordinal
Gender	Gender of respondent	1 = Male, 2 = Female	Nominal
Satisfaction	Level of satisfaction	1 = Very Unsatisfied, 5 = Very Satisfied	Ordinal

Input Data in Excel:
- Create a new sheet called “Survey Data.”
- Enter the 30 completed survey responses in rows, with each variable as a column header.
- Each row represents one survey response.

Task 2: Conduct Descriptive Statistics (Mean, Median, Mode, Range, etc.)

Calculate Descriptive Statistics for at least 4 questions:
For each selected question, calculate the following:
- Mean: The average of responses.
- Median: The middle value when data is ordered.
- Mode: The most frequent response.
- Range: The difference between the highest and lowest values.
Steps:
- For example, for a numerical question like “Age”, use Excel’s AVERAGE, MEDIAN, MODE, and MAX-MIN functions.
- For a categorical variable (e.g., Gender), you can calculate frequencies using COUNTIF to get the mode or distribution.
Example Formulae in Excel:
- Mean: =AVERAGE(B2:B31)
- Median: =MEDIAN(B2:B31)
- Mode: =MODE.SNGL(B2:B31)
- Range: =MAX(B2:B31)-MIN(B2:B31)
Perform this for at least four different survey questions.

Task 3: Conduct Inferential Statistics

You will conduct one Chi-Square, one T-test, and one Correlation. Below are steps for each test:

Chi-Square Test:
- Purpose: Used to assess if there is an association between two categorical variables.
- For example, testing if “Gender” (Male/Female) is associated with “Satisfaction Level” (1-5).
Steps in Excel:
- Create a contingency table for the two categorical variables.
- Use Excel’s CHISQ.TEST() function.
- Example: =CHISQ.TEST(actual_range, expected_range)
Interpretation: If the p-value from the test is below 0.05, we reject the null hypothesis (i.e., the variables are associated).
T-Test:
- Purpose: Used to compare the means of two independent groups.
- Example: Compare “Satisfaction Level” between “Male” and “Female” respondents.
Steps in Excel:
- Use =T.TEST(range1, range2, tails, type) where tails is 2 for two-tailed test and type is 2 for unequal variance.
- Example: =T.TEST(A2:A15, A16:A30, 2, 2)
Interpretation: If p-value < 0.05, conclude there is a significant difference between groups.
Correlation:
- Purpose: Measure the relationship between two continuous variables (e.g., Age and Satisfaction).
Steps in Excel:
- Use =CORREL(range1, range2) to calculate the correlation coefficient.
Interpretation: A value close to 1 or -1 indicates a strong positive or negative correlation, respectively.

Task 4: Create Visualizations

Visual aids make data analysis easier to understand. You need to create at least one table and three figures (charts/graphs).

Create a Table:
- You can create a summary table to present the descriptive statistics (e.g., Mean, Median, Mode) for different questions. Example:
Question Mean Median Mode Range

Satisfaction Level 4.2 4 5 4

Age (in years) 28 30 25 15

Gender (Male/Female) – – Female –
Create Figures:
- Bar Chart: For categorical data like “Gender” or “Satisfaction Level”, use a bar chart.
- Pie Chart: To visualize proportions, such as gender distribution or satisfaction levels.
- Histogram: For continuous data, like Age or Satisfaction scores.
Steps:
- Select your data range and choose Insert > Chart > Choose your preferred type (bar, pie, etc.).
- Customize the chart for clarity (e.g., adding labels and titles).

Question	Mean	Median	Mode	Range
Satisfaction Level	4.2	4	5	4
Age (in years)	28	30	25	15
Gender (Male/Female)	–	–	Female	–

Final Steps: Submit Your Files

Save the Excel file with the completed data, descriptive stats, inferential tests, and visualizations.
Alternatively, if you need to submit a Word document, you can include:
- Descriptive Statistics and Interpretations (written out)
- Explanation of Inferential Tests (Chi-Square, T-test, and Correlation)
- A screenshot or embedded table/figure from Excel.

Make sure to review the work, check that the originality report is below 15%, and ensure the proper file format before submitting.

This guide should provide you with the structure and steps needed to tackle the assignment efficiently. Let me know if you’d like help with the Excel formulas or specific tasks!

Data Analysis Assignment: Excel, Statistics, and Visualization