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Course Update: November, 2016.
Get marketable and highly sought after skills in this course that will substantially increase your knowledge of data analytics, with a focus in the area of significance testing, an important tool for A/B testing and product assessment.
Many tests covered, including three different t tests, two ANOVAs, post hoc tests, chisquare tests (great for A/B testing), correlation, and regression. Database management also covered!
Two indepth examples provided of each test for additional practice.
This course is great for professionals, as it provides step by step instruction of tests with clear and accurate explanations. Get ahead of the competition and make these tests important parts of your data analytic toolkit!
Students will also have the tools needed to succeed in their statistics and experimental design courses.
Data Analytics is an rapidly growing area in high demand (e.g., McKinsey)
Statistics play a key role in the process of making sound business decisions that will generate higher profits. Without statistics, it's difficult to determine what your target audience wants and needs.
Inferential statistics, in particular, help you understand a population's needs better so that you can provide attractive products and services.
This course is designed for business professionals who want to know how to analyze data. You'll learn how to use IBM SPSS to draw accurate conclusions on your research and make decisions that will benefit your customers and your bottom line.
Use Tests in SPSS to Correctly Analyze Inferential Statistics
Analyze Research Results Accurately to Make Better Business Decisions
With SPSS, you can analyze data to make the right business decisions for your customer base. And by understanding how to use inferential statistics, you can draw accurate conclusions about a large group of people, based on research conducted on a sample of that population.
This easytofollow course, which contains illustrative examples throughout, will show you how to use tests to assess if the results of your research are statistically significant.
You'll be able to determine the appropriate statistical test to use for a particular data set, and you'll know how to understand, calculate, and interpret effect sizes and confidence intervals.
You'll even know how to write the results of statistical analyses in APA format, one of the most popular and accepted formats for presenting the results of statistical analyses, which you can successfully adapt to other formats as needed.
Contents and Overview
This course begins with a brief introduction before diving right into the One Sample t Test, Independent Samples t Test, and Dependent Samples t Test. You'll use these tests to analyze differences and similarities between sample groups in a population. This will help you determine if you need to change your business plan for certain markets of consumers.
Next, you'll tackle how to use ANOVA (Analysis of Variance), including Posthoc Tests and Levene's Equal Variance Test. These tests will also help you determine what drives consumer decisions and behaviors between different groups.
When ready, you'll master correlation and regression, as well as the chisquare. As with all previous sections, you'll see illustrations of how to analyze a statistical test, and you'll access additional examples for more practice.
Finally, you'll learn about data management in SPSS, including sorting and adding variables.
By the end of this course, you'll be substantially more confident in both IBM SPSS and statistics. You'll know how to use data to come to the right conclusions about your market.
By understanding how to use inferential statistics, you'll be able to identify consumer needs and come up with products and/or services that will address those needs effectively.
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Lecture 1  10:51  
The one sample t test is covered in this lecture. The SPSS data files (for the entire course) are available under "downloadable materials" in this lecture. Also, a pdf file of the results (the output file) is also available. The output file for this lecture is located below and is titled, "One sample t example 1 output" All other output files are located within their respective lecture. For example, the output file for the second example on the one sample t test is located in the lecture "one sample t_example 2". SPSS Data file for this video: one sample t_example 1. 

Lecture 2  09:43  
An overview of the course is provided in this lecture, including highlighting how to download the data files and the output files for the course. 

Section 1: One Sample t Test  

Lecture 3  04:40  
This lecture continues with the example from the previous lecture, with a focus on how to interpret the section of the output labeled, "95% confidence interval of the difference". Learning Tip: If the confidence interval includes the value of zero, the test is not statistically significant. If it does not include zero, the test is statistically significant. SPSS Data file for this video: one sample t_example 1. 

Lecture 4  03:48  
In this lecture, how to calculate and interpret the effect size for the one sample t test is presented. Learning Tip: Cohen's effect size standards for t are: small = .20, medium = .50, large = .80. The effect size indicates the number of standard deviation units of a difference that exist between two groups. For example, an effect size of 1.00 indicates one standard deviation of a difference between the sample mean (the treated group) and the population mean (the untreated group). SPSS Data file for this video: one sample t_example 1. 

Lecture 5  07:20  
In this lecture, a second example utilizing the one sample t test is illustrated. Learning Tip: Try running and interpreting the one sample t test on your own (using the data file "one sample t_example 2.sav") prior to watching this lecture. This will help both increase your understanding and retention of the subject matter. SPSS Data file for this video: one sample t_example 2. 

Section 2: Independent Samples t Test  
Lecture 6  15:45  
In this lecture, the first example on the independent samples t test is covered. Learning Tip: The independent samples t test is used when two separate or unrelated groups are compared. Mathematically, unrelated groups are known as being "independent". SPSS Data file for this video: independent t_example 1. 

Lecture 7  01:49  
In this lecture, the confidence interval for the independent samples t test is covered. Learning Tip: If the confidence interval includes the value of zero, the test is not statistically significant. If it does not include zero, the test is statistically significant. SPSS Data file for this video: independent t_example 1. 

Lecture 8  05:25  
In this lecture, the effect size for the independent samples t test is covered. Learning Tip: Cohen's effect size standards for t are: small = .20, medium = .50, large = .80. The effect size indicates the number of standard deviation units of a difference that exist between two groups. For example, an effect size of .50 indicates onehalf of a standard deviation difference between the two groups. SPSS Data file for this video: independent t_example 1. 

Lecture 9  05:28  
In this lecture, the second example on the independent samples t test is covered. SPSS Data file for this video: independent t_example 2. 

Section 3: Dependent Samples t Test  
Lecture 10  10:35  
In this lecture the dependent samples t test is covered. Learning Tip: The dependent samples t test is used when the two samples are naturally dependent. This usually consists of the same people in each group such as a when people take a pretest and then a posttest. However, instead of being the same people, the two groups can also be related, such as with identical twins. The key with this test is that the groups are naturally related in some way (as opposed to the independent samples t test). SPSS Data file for this video: dependent t_example 1. 

Lecture 11  02:30  
In this lecture, the effect size for the dependent samples t test is covered. Learning Tip: Cohen's effect size standards for t are: small = .20, medium = .50, large = .80. The effect size indicates the number of standard deviation units of a difference that exist between the two groups. For example, an effect size of .25 indicates onequarter of a standard deviation difference between the two groups. SPSS Data file for this video: dependent t_example 1. 

Lecture 12  07:25  
In this lecture, the second example on the dependent samples t test is covered. SPSS Data file for this video: dependent t_example 2. 

Quiz 1  4 questions  
A very important (and marketable) skill is knowing how to select the correct test for a set of data. The questions on this quiz all involve t tests. Your job is to select the most appropriate test for each situation. Good luck! 

Section 4: ANOVA  Analysis of Variance  
Lecture 13  10:16  
In this lecture, the oneway between subjects ANOVA is covered. Learning Tip: The oneway between subjects ANOVA may be used when 2 or more separate or unrelated groups are compared. Many people think of this test being used with 3 or more groups, but it is perfectly fine to use it for two groups as well. (Either the ANOVA or the independent samples t test can be used when there are two unrelated groups). SPSS Data file for this video: one way ANOVA_example 1. 

Lecture 14  10:13  
In this lecture, posthoc tests are covered. Learning Tip: "Posthoc" means "after the fact"; posthoc tests are typically conducted after a significant result is found for the ANOVA. If the ANOVA is not significant, then posthoc tests typically are not interpreted. While there are many different posthoc tests available, Tukey's test is covered here as (1) it is one of the more commonly used posthoc tests and (2) research has shown that Tukey's test does a good job at keeping the overall alpha level at .05 (assuming one is using an alpha of .05). SPSS Data file for this video: one way ANOVA_example 1. 

Lecture 15  08:27  
In this lecture, a second example using the oneway between subjects ANOVA is covered. Posthoc tests are also covered in this lecture. SPSS Data file for this video: one way ANOVA_example 2. 

Lecture 16  12:54  
In this video, we take a look at Levene's test of equal variances. 

Lecture 17  06:03  
In this video we take a look at the relationship between the independent samples t test and the oneway between subjects ANOVA when there are two groups. You might be surprised what you find! 

Lecture 18  11:14  
In this lecture, the oneway within subjects ANOVA is covered. Learning Tip: The oneway within subjects ANOVA may be used when 2 or more dependent or related groups are compared. Many people think of this test being used with 3 or more groups, but it is perfectly fine to use it for two groups as well. (Either the within ANOVA or the dependent samples t test can be used when there are two related groups). SPSS Data file for this video: one within ANOVA_example 1. 

Lecture 19  12:21  
In this lecture, posthoc tests are covered. The appropriate posthoc test to use for the within subjects ANOVA is the dependent samples t test, with a separate t test used for each pair of groups. SPSS Data file for this video: one within ANOVA_example 1. 

Lecture 20  08:14  
A second example on the oneway within subjects ANOVA is covered here. SPSS Data file for this video: one within ANOVA_example 2. 

Section 5: Correlation and Regression  
Lecture 21  14:23  
This lecture covers the Pearson r correlation coefficient. How to produce a scatterplot of the two variables in SPSS is also illustrated towards the end of the lecture. SPSS Data file for this video: Correlation_example 1. 

Lecture 22  06:26  
This lecture covers a second example on correlation. SPSS Data file for this video: Correlation_example 2. 

Lecture 23  17:07  
This lecture covers simple regression, which is used when there is one predictor (independent variable) and one criterion (dependent variable). SPSS Data file for this video: Regression_example 1. 

Lecture 24  14:09  
A second example using simple regression is covered in this lecture. SPSS Data file for this video: Regression_example 2. 

Section 6: ChiSquare  
Lecture 25  08:25  
In this lecture the chisquare goodness of fit test is covered. SPSS Data file for this video: Chi Square GFI_example 1. 

Lecture 26  13:03  
In this lecture, a second example on the chisquare goodness of fit test is provided. SPSS Data file for this video: Chi Square GFI_example 2. 

Lecture 27  21:38  
In this lecture, the chisquare test of independence is covered. SPSS Data file for this video: Chi Square Test of Independence_example 1. 

Lecture 28  12:20  
This lecture provides a second example on the chisquare test of independence. SPSS Data file for this video: Chi Square Test of Independence_example 2. 

Section 7: Bonus Material  Data Management in SPSS  
Lecture 29  04:14  
In this lecture, how to add a number of variables together to create a total score using the compute procedure is illustrated. SPSS Data file for this video: Compute Procedure_Manual Add. 

Lecture 30  06:27  
In this lecture, how to add a number of variables together to create a total score using the compute procedure is illustrated. Whereas the previous lecture manually added the variables (using SPSS), in this lecture the variables are added together using the SUM function in SPSS. SPSS Data file for this video: Compute Procedure_Sum Function. 

Lecture 31  05:16  
This lecture illustrates how to use the sort command in SPSS. The sort command is illustrated first on a single variable in SPSS; afterwards, a set of cases is sorted on two variables simultaneously. SPSS Data file for this video: Sort Example. 

Section 8: Conclusion  
Lecture 32 
Course Conclusion

02:32 
Quantitative Specialists (QS) was founded by an awardwinning university instructor who has taught statistics courses for over 15 years. At QS, we are passionate about all things statistical, especially in helping others understand this oftenfeared subject matter. Our focus is in helping you to succeed in all your statistics work!