(Answered) Working with Descriptive Statistics

(Answered) Working with Descriptive Statistics

(Answered) Working with Descriptive Statistics 150 150 Prisc

Working with Descriptive Statistics

This course helps you develop a basic understanding of statistics. This course addresses two distinct types, descriptive and inferential. In this assignment, you will use a software program that makes it easy to analyze data using specific tests. This assignment will give you practice with mean, median, mode, frequency, range, and standard deviation.

General Requirements:

Use the following information to ensure successful completion of the assignment:

  • Before beginning this assignment, view the SPSS tutorial videos provided as topic materials.
  • Doctoral learners are required to use APA style for their writing assignments. The APA Style Guide is located in the Student Success Center.
  • This assignment uses a rubric. Review the rubric prior to beginning the assignment to become familiar with the expectations for successful completion.
  • You are not required to submit this assignment to LopesWrite.


SPSS Output Open the SPSS program, enter the data below using the steps, and create an output (as in the tutorial videos) with the following results highlighted:

  1. Determine the statistics for each gender as follows: Frequency Counts and Percentages.
  2. Determine the statistics for each blood sugar as follows: Mean, Standard Deviation, Minimum, and Maximum.
  3. Graphing and Descriptive Stats in SPSS: Create a bar graph with gender (axis X) and blood sugar (axis Y). Create a histogram of the blood sugar.

Data Set

Use the following data set for this assignment:

You have a group of patients observed with a diagnosis of diabetes, and their blood sugar levels are listed below based on gender.

Men: 74, 71, 75, 248, 388, 505, 42, 212, 56

Female: 62, 68, 61, 71, 68, 80, 390, 148, 43

  1. Open SPSS and open a new database.
  2. Go to the Variable View.
  3. In the first row, label the name as Gender. Enter Gender as the Label Field. In the Values drop down, Enter 1 = Male; 2 = Female.
  4. In the second row, label the name as BloodSugar. Enter Blood Sugar in the Label Field.
  5. Go to the Data View and enter the data. Use 1’s for the nine Males and 2’s for the nine Females in the first column. Enter the blood sugars in the second column next to or aligned to the respective gender.
  6. Go to the menu and click Analyze. Click Descriptive Statistics, then click Frequencies, and add Gender and Patient Weight to the field. Click Charts and select Histogram. Indicate to provide a normal curve. Click OK. Review and save the Output.
  7. Go to the menu and click Analyze. Click Descriptive Statistics, then click Descriptives and add Blood Sugar to the field. Review and save Output.
  8. Go to the menu and click the Graph. Click Chart Builder. Click OK to open. Select the Bar Graph and move to the field. Move the Gender Variable to the x-axis and the Blood Sugar to the y- Click OK. Review and save the Output.


Write a 250-500 word summary of your results and how this statistical analysis may be applied to your prospectus. Provide a histogram of the blood sugars graphed. Provide a bar graph with the gender on the x-axis and blood sugar levels on the y-axis. Add your SPSS statistical outputs as an Appendix to this summary.

Sample Answer

Working with Descriptive Statistics

Descriptive statistics are crucial for summarizing a study sample without drawing inferences founded on probability theory. Spriestersbach et al. (2009) argue that using tools such as measures of central tendency, percentages, and frequency distribution tables to describe the study population is an example of descriptive statistics. Therefore, through simple quantitative measures such as visual summaries (box plots and histograms), means, and percentages, descriptive statistics can help in data summarization. In addition, summarizing the relationship between variables can be achieved using scatter plots where multivariate or bivariate analysis is applicable (Kaliyadan & Kulkarni, 2019).

Summary of the Results

The output window, statistics, indicated the number of valid and missing variables. There were 18 participants in the data set, and there were 18 blood sugar results. There were no missing variables. The output window, gender, indicated the percentage and cumulative percentage of each gender in the sample. The output window, blood sugar, indicated the frequency and percentage of each blood sugar in the data set.

Statistical Analysis

Based on the data output data, of the 18 participants in the data set, 50 percent were male, and 50 percent were female. The mean blood sugar was 147.89, and the standard deviation was 142.14. The minimum blood sugar value was 42, while the maximum value was 505.

Bar Graphs and Histograms

The histogram of gender frequency with the normal distribution curve had insignificant use. This is because males and females are not continuous data, and the normal distribution curve did not provide much information to analyze the data. Thus, the gender histogram only showed the same number of males and females in the data set. However, the histogram of blood sugar and frequency with the normal distribution curve was more practical. The histogram helped to visualize that blood sugars up to 100 occurred most frequently. The blood sugars were not normally distributed around the mean but were skewed to the left of the mean. While there were no blood sugars between 400 and 500 on the blood sugar histogram, the simple bar chart displayed blood sugar variation with gender.

Application of Descriptive Statistical Analysis to Prospectus

Kaur et al. (2018) claim that descriptive statistical analysis is essential in summarizing the overall data before making inferences in the prospectus. Besides, descriptive statistics can present data using frequency distribution tables, illustrations, or visual displays such as histograms, frequency diagrams, box plots, and bar charts. Scatter diagrams and pie charts are valuable tools for presenting the data visually. This is further supported by Kaliyadan and Kulkarni (2019). They infer that information on the data that includes central tendency measures (mean, median, and mode) and measures of variation (variance, range, and standard deviation) can be used to provide an overview of the data.