Trying to help a friend: How do I analyse this? (Simple Statistics)

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In summary, the conversation is about using SPSS statistics software to compare the weight loss numbers of two groups, Gym A and Gym B. The person is not familiar with statistics and needs help calculating a t-test to determine which group has the highest level of weight loss. They have just downloaded a 1-month trial of SPSS and are looking for suggestions on how to use it.
  • #1
nukeman
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Hey, how can I run a analyse with SPSS statistics software for this data? (I am not a stats guy at all, and not sure about using SPSS)

Group 1 is "Gym A"

Group 2 is "Gym B"

And the values are just the weight loss numbers.

All we are trying to do is compare Gym A (group 1) to Gym B (group 2). Seeing what one is better?

Any help?

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  • #2
What are you trying to calculate, precisely? (If you don't know the test and how it works you won't be able to interpret the results, even if we tell you what buttons to press.)
 
  • #3
I am pretty sure I need to calculate a t-test (comparing two means)

All I am trying to do with SPSS is show which group has the highest level of weight loss. I am just not sure how to use SPSS to well (Just downloaded the 1 monther of SPSS)
 
  • #4
Any ideas?
 
  • #5


I understand your desire to analyze this data and help your friend determine which gym may be more effective for weight loss. The first step in analyzing this data would be to determine the appropriate statistical test to use. Since you are comparing two groups, a t-test would be a suitable option.

To run this analysis using SPSS, you would first need to input the data into the software. You can do this by creating two columns, one for Group 1 and one for Group 2, and entering the weight loss numbers for each participant in their respective group. Once the data is entered, you can use the t-test function in SPSS to compare the two groups and determine if there is a significant difference in weight loss between Gym A and Gym B.

If you are not familiar with using SPSS, I would suggest seeking assistance from someone who is more experienced with statistical analysis. Alternatively, there are many online resources and tutorials available to help guide you through using SPSS for data analysis. I would also recommend consulting with a statistician to ensure that the analysis is conducted correctly and the results are interpreted accurately.

Overall, analyzing data can be complex and it is important to use the appropriate statistical methods and tools to ensure accurate and reliable results. I hope this helps guide you in your analysis and I wish you and your friend the best of luck in finding the most effective gym for weight loss.
 

1. How do I start analyzing the data?

The first step in analyzing data is to understand what type of data you have and what is the purpose of your analysis. This will help you determine which statistical methods to use and how to approach the analysis.

2. What are some common statistical tests I can use?

Some common statistical tests include t-tests, ANOVA, regression analysis, and chi-square test. The choice of which test to use depends on the type of data you have and the research question you are trying to answer.

3. How do I interpret the results of my analysis?

Interpreting the results of your analysis involves understanding the statistical significance, the effect size, and the confidence interval of your findings. It is important to also consider the context of your analysis and the limitations of your data.

4. What should I do if I am unsure about my analysis?

If you are unsure about your analysis, it is always best to consult with a statistician or a colleague who has expertise in statistics. They can provide guidance on the appropriate methods to use and help interpret the results.

5. How can I ensure the accuracy of my analysis?

To ensure the accuracy of your analysis, it is important to carefully check your data for any errors or outliers. It is also helpful to conduct sensitivity analyses and replicate your analysis using different methods to validate your findings.

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