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Using AIU’s survey responses from the AIU data set, complete the following requirements in the form of a 2-page report:
TEST #1
Perform the following two-tailed hypothesis test, using a .05 significance level:
- Intrinsic by Gender
- State the null and an alternate statement for the test
- Use Microsoft Excel (Data Analysis Tools) to process your data and run the appropriate test.
- Copy and paste the results of the output to your report in Microsoft Word.
- Identify the significance level, the test statistic, and the critical value.
- State whether you are rejecting or failing to reject the null hypothesis statement.
- Explain how the results could be used by the manager of the company.
TEST #2
Perform the following two-tailed hypothesis test, using a .05 significance level:
- Extrinsic variable by Position Type
State the null and an alternate statement for the testUse Microsoft Excel (Data Analysis Tools) to process your data and run the appropriate test.Copy and paste the results of the output to your report in Microsoft Word.Identify the significance level, the test statistic, and the critical value.State whether you are rejecting or failing to reject the null hypothesis statement.Explain how the results could be used by the manager of the company.
GENERAL ANALYSIS (Research Required)
Using your textbook or other appropriate college-level resources:
- Explain when to use a t-test and when to use a z-test. Explore the differences.
- Discuss why samples are used instead of populations.
The report should be well written and should flow well with no grammatical errors. It should include proper citation in APA formatting in both the in-text and reference pages and include a title page, be double-spaced, and in Times New Roman, 12-point font. APA formatting is necessary to ensure academic honesty.
Be sure to provide references in APA format for any resource you may use to support your answers.
>SURVEY
1 1 1 5.4 Gender 6.4 4.8 4.7 1 4.7 4.7 4.7 2 5.2 5.4 5.4 Age 5.3 1 6.4 6.4 3 5.2 5.2 6.4 3 5.3 6.4 5.2 Position 5.4 4.7 1 1 1 1 1 3 4.9 6.4 5.5 5.4 5.4 5.6 5.4 1 = Least Satisfied 1 3 3 1 1 4.9 5.2 4.6 4.2 1 3 3 1 1 4.9 5.2 4.6 4.2 1 3 3 1 1 4.9 5.2 4.6 4.2 1 2 2 1 1 5.2 4.7 5.6 4.5 1 1 2 2 1 4.9 5.3 5.7 2.3 1 3 3 1 1 4.9 5.2 4.6 4.2 1 2 2 1 3 4.9 6.4 5.5 5.4 1 1 3 2 1 4.9 5.2 4.6 4.2 2 2 2 1 1 5.2 4.7 5.6 4.5 2 1 3 2 1 4.9 4.7 5.6 4.5 2 2 2 1 1 5.2 4.7 5.6 4.5 2 3 3 1 1 4.9 5.2 4.6 4.2 5.4 6.8 6.4 2.2 6.4 6.5 5.5 >Welcome Info
!
Assignment List for more details.
mailto:lemurray@faculty.aiuonline.edu 1 0 . 4 0 2 5
1 3 2 3 9 Visit Code 1 9 2 12 3 8 Gender 0 2 1 3 1 1 2 3 5 7
6 $109 2 1 8 ,
mailto:http://youtu.be/GPqkKMi9yM4 $109 s. This particular test is chosen because the variances (the dispersion measures that are the squares of the standard deviation) are not equal. You can determine whether the variances are equal or not by using the Microsoft Excel Toolpak to get Descriptive Statistics for each of the variables’ values in the same way that you determined Descriptive Statistics in Unit 1. If the variances ARE equal, then you would choose the t-test for Equal Variances. The reason we chose a t-test is because the values for EACH of the variables we are using, number less than 30. If the values number greater than 30, you would choose a z-test for equal means.
Difference slot in the Toolpak because that is exactly what your hypothesis statement indicates: There is no difference in the means of the two variables being compared.
618.28
9 12 0 12 2. Alpha values, 5. An explanation as to what that decision means in the practical sense. Males Females Alpha is another way of saying significance level.
istic: 0.52
: 0.61
A COMPARISON OF P VALUES TO ALPHA VALUES. REGARDLESS, THE PROCESS SHOULD BE THE SAME.
t-Test: Two-Sample Assuming Unequal Variances Observations 9 12 3644646
0.05 Therefore we fail to reject Ho
2
Gender
Age
Department
Position
Tenure
Job Satisfaction
Intrinsic
Extrinsic
Benefits
1
3
4.9
6.4
5.5
5.4
1 3 3 1 1 4.9
5.2
4.6
4.2
KEY TO SURVEY
1 1 2 2 1 4.9
5.3
5.7
2.3
1 1 3 1 1 5.2
4.7
5.6
4.5
Demographics
1 3 3 1 1 4.9 5.2 4.6 4.2
1 1 2 1 2
6.9
4.1
4.8
1 1 1 1 3
6.8
Male
1 1 2 1 2
2.2
Female
1 1 1 1 3
3.4
1 2 2 1 2
6.5
2.9
3.7
16 – 21
1 2 1 1 3 4.8 5.3 5.5 5.2 2
22 – 49
1 2 2 1 3
3.8
3.9
50 – 65
1 2 1 1 2 5.2 5.2
5.8
Department
1 2 2 1 2 3.4 5.2 5.2 5.3 1
Human Resources
1 3 1 1 1 5.5 6.4 5.8 4.7 2
Information Technology
1 3 2 1 1
2.4
5.9
Administration
1 3 3 1 1
3.5
1 3 3 1 2 6.9 4.7 5.7 4.7 1
Hourly Employee (Overtime Eligible)
1 3 3 1 2 5.5 6.4 5.8 5.4 2
Salaried Employee (No Overtime)
1 3 3 1 2 5.2 5.2 5.6 6.4
Tenure With Company
1 3 3 1 2 5.7
1.2
Less than 2 years
1 1 3 1 2 5.5 2.4 2.3 5.2 2
2 to 5 years
1 1 1 1 2 4.9 5.3 5.6 5.4 3
Over 5 Years
1 3 3 1 1 4.9 5.2 4.6 4.2
Four Survey Measures
1 1 2 2 1 4.9 5.2 4.6 4.2
1 1 3 1 1 5.2 6.4 5.5 3.5
SURVEY MEASURE #1 OVERALL JOB SATISFACTION (Scale 1-7)
1 3 3 1 1 4.9 5.2 4.6 4.2
1 = Least Satisfied
1 2 2 1 1 5.2 5.3 5.7 2.3
7 = Most Satisfied
1 2 1 1 2 5.2 4.7 5.6 4.5
SURVEY MEASURE #2 INTRINSIC JOB SATISFACTION (Scale 1-7)
1 3 3 1 2 4.9 6.4 5.5 5.4
1= Least Satisfied
1 2 2 1 3 5.9 5.2 4.6 4.2
7= Most Satisfied
1 1 3 2 1 4.9 4.7 5.6 4.5
SURVEY MEASURE #3 EXTRINSIC JOB SATISFACTION (Scale 1-7)
1 1 1 1 3
3.2
1 3 3 1 1 4.9 6.4 4.6 4.2 7 = Most Satisfied
1 1 3 2 1 4.9 4.7 5.6 4.5
SURVEY MEASURE #4 BENEFITS (Scale 1-7)
1 1 3 1 3 5.2 5.2 5.7 4.2 1= Least Satisfied
1 2 3 1 3 4.9 5.2 4.6 4.2 7= Most Satisfied
1 3 2 1 1 4.9 6.4 5.5 3.5
1 2 2 1 1 5.2 4.7 5.6 4.5
1 2 2 2 3 4.9 5.4 5.6 5.4
1 1 3 2 3 4.9 5.2 4.6 4.2
1 2 2 1 2 5.2 6.4 5.5 3.5
1 1 1 1 2 5.2 5.3 5.7 2.3
1 3 2 1 1 5.2 4.7 5.6 4.5
1 1 3 1 2 4.9 6.4 5.5 5.4
1 2 1 2 3 5.9 5.2 4.6 4.2
1 2 1 1 1 4.9 4.7 5.6 4.5
1 1 1 2 3 4.9 6.4 5.5 5.4
1 1 2 2 1 4.9 5.3 5.7 2.3
1 3 3 1 1 4.9 5.2 4.6 4.2
1 2 2 1 1 5.2 4.7 5.6 4.5
1 2 2 1 3 4.9 5.4 5.6 5.4
1 1 1 1 3 3.2 6.4 4.6 4.2
1 2 1 2 2 5.2 4.7 5.6 4.5
1 2 2 1 3 4.9 6.4 5.5 5.4
1 2 2 1 1 5.2 4.7 5.6 4.5
1 2 2 1 3 4.9 5.4 5.6 5.4
1 1 1 1 3 3.2 6.4 4.6 4.2
1 1 3 1 1 5.2 4.7 5.6 4.5
1 3 3 2 1 4.9 5.2 4.6 4.2
1 2 3 2 3 4.9 4.7 5.5 5.4
1 2 2 2 1 3.2 5.4 4.6 4.2
1 2 2 1 1 4.9 6.4 5.4 4.5
1 1 3 1 1 5.2 4.7 5.6 4.5
1 3 3 1 1 4.9 5.2 4.6 4.2
1 2 2 1 1 5.2 4.7 5.6 4.5
1 2 1 1 2 5.2 4.7 5.6 4.5
1 3 3 1 2 4.9 5.4 5.6 5.4
1 2 2 1 3 5.9 3.7 5.5 4.2
1 1 3 2 1 4.9 5.2 4.6 4.2
1 1 1 1 3 3.2 6.4 5.5 3.5
1 1 2 2 1 4.9 5.3 5.7 2.3
1 1 3 1 3 5.2 4.7 5.6 4.5
1 3 3 1 1 4.9 5.2 4.6 4.2
1 2 2 1 1 5.2 4.7 5.6 4.5
1 2 2 1 3 4.9 5.4 5.6 5.4
1 1 1 1 3 3.2 6.4 4.6 4.2
1 2 1 1 2 5.2 4.7 5.6 4.5 1 3 3 1 1 4.9 5.2 5.7 4.2
1 1 1 1 3 3.2 6.4 5.5 3.5
1 3 3 1 1 4.9 5.2 4.6 4.2
1 1 2 2 1 4.9 5.3 5.7 2.3
1 3 3 1 1 4.9 5.2 4.6 4.2
1 1 2 2 1 4.9 5.3 5.7 2.3
1 1 3 1 3 5.2 4.7 5.6 4.5 2 2 2 1 3 4.9 6.4 5.5 5.4
2 3 3 1 1 4.9 5.2 4.6 4.2
2 2 2 1 1 5.2 4.7 5.6 4.5
2 2 2 1 3 4.9 5.4 5.6 5.4
2 1 1 1 3 3.2 6.4 4.6 4.2
2 2 1 1 2 5.2 4.7 5.6 4.5
2 3 3 1 1 4.9 5.2 5.7 4.2
2 1 3 2 1 4.9 5.2 4.6 4.2
2 1 1 1 3 3.2 6.4 5.5 3.5
2 2 2 1 3 4.9 5.4 5.6 5.4
2 2 1 1 2 5.2 4.7 5.6 4.5 2 3 3 1 2 4.9 6.4 5.5 5.4
2 2 2 1 3 5.9 5.2 4.6 4.2
2 1 3 2 1 4.9 4.7 5.6 4.5
2 1 1 1 3 3.2 5.4 5.6 5.4
2 3 3 1 1 4.9 6.4 4.6 4.2
2 1 3 1 3 5.2 5.2 5.7 4.2
2 2 3 1 3 4.9 5.2 4.6 4.2
2 3 2 1 1 4.9 6.4 5.5 3.5
2 2 2 2 3 4.9 5.4 5.6 5.4
2 1 3 2 3 4.9 5.2 4.6 4.2
2 2 2 1 2 5.2 6.4 5.5 3.5
2 1 1 1 2 5.2 5.3 5.7 2.3
2 3 2 1 1 5.2 4.7 5.6 4.5
2 1 3 1 2 4.9 6.4 5.5 5.4
2 2 1 2 3 5.9 5.2 4.6 4.2
2 2 1 1 1 5.7 4.7 6.9
6.7
2 2 1 1 1
5.1
2.1
2 3 2 1 1
3.1
2.5
2 2 2 2 3 2.3 4.7 3.4 3.1
2 2 2 2 1
2.8
2 1 2 2 1 6.9 5.2 6.9 5.7
2 1 2 2 1 6.8 4.7 6.8 4.8
2 3 3 1 1 2.2 5.4 2.2 4.6
2 2 3 1 1 3.4 6.4 3.4 2.2
2 2 3 1 1 6.5 4.7 6.5 2.5
2 2 3 1 2 4.8 5.2 5.5
2.6
2 2 3 1 2 3.8 4.7 2.4 2.8
2 1 1 1 3 5.2 4.7 3.5 2.9
2 1 1 2 1 3.4 5.4 6.9 5.4
2 3 1 2 3 5.7 6.4 5.5 3.1
2 2 1 2 1 5.1 5.3 5.2 2.5
2 2 1 2 1 3.1 2.3 5.7 3.8
2 3 1 2 3 2.3 4.5 5.5 3.4
2 2 2 2 3 2.8 5.4 2.8
3.6
2 1 2 2 1 6.9 4.2 3.4 2.5
2 1 2 2 1 6.8 4.5 3.9
6.6
2 3 2 2 3 2.2 3.1 2.2 6.8
2 1 2 1 3 3.4 3.5 5.4 6.5
2 1 3 1 2 6.5 3.7 2.3 6.5
2 1 3 1 1 4.8 4.2 2.5 6.6
2 2 3 1 1 3.8 5.8 2.1 6.8
2 3 3 1 3 5.2 6.7 4.5 4.5
2 3 3 1 3 3.4 2.1 4.1 4.2
2 1 1 1 3 2.4 3.1 4.7 4.8
2 1 1 1 2 3.5 5.5 5.4 2.8
2 2 1 1 2 6.9 5.7 2.9 2.9
2 3 1 2 1 5.5 4.8 5.5 5.4
2 3 2 2 1 5.2 4.6 3.9 3.1
2 3 2 2 3 5.7 4.7 5.8 2.5
2 2 2 2 3 5.5 5.2 5.2 3.8
2 1 2 2 1 2.8 4.7 5.8 3.4
2 2 2 2 2 3.4 4.7 5.9 3.6
2 3 2 2 2 3.9 5.4 6.4 2.5
2 3 1 2 1 4.8 3.7 5.7 6.6
2 1 1 2 1 3.8 5.2 5.8 6.8
2 2 1 2 3 5.2 6.4 5.6 6.5
2 3 1 2 3 3.4 5.2 5.4 6.5
2 3 2 1 1 5.7 5.3 5.4 6.6
2 2 2 1 3 5.1 4.7 2.3 6.8
2 2 2 1 2 3.1 6.4 2.5 4.5
2 1 2 1 1 2.3 5.2 2.1 4.2
2 1 2 1 3 2.8 4.7 4.5
4.3
2 1 2 1 2 6.9 5.4 4.1 4.8
2 1 1 1 3 6.8 6.4 4.8 4.7
2 1 2 1 2 2.2 4.7 4.7 4.7
2 1 1 1 3 3.4 5.2 5.4 5.4
2 2 2 1 2 6.5 5.3 2.9 3.7
2 2 1 1 3 4.8 5.3 5.5 5.2
2 2 2 1 3 3.8 6.4 3.9 6.4
2 2 1 1 2 5.2 5.2 5.8 5.2
2 2 2 1 2 3.4 5.2 5.2 5.3
2 3 1 1 1 5.5 6.4 5.8 4.7
2 3 2 1 1 2.4 5.2 5.9 6.4
2 3 3 1 1 3.5 5.3 6.4 5.2
2 3 3 1 2 6.9 4.7 5.7 4.7
2 3 3 1 2 5.5 6.4 5.8 5.4
2 3 3 1 2 5.2 5.2 5.6 6.4
2 3 3 1 2 5.7 1.2 5.4 4.7
2 1 3 1 2 5.5 2.4 2.3 5.2
2
Welcome to Unit
4
We have officially passed the half way point in the class! It is hard to believe but we are on to unit 4.
Last week we focused on samples and sample sizes. It is important to realize that when you get information from a sample you are actually getting an ESTIMATE of the value for the entire population, using the sample as a representation for the population. This week we will focus on comparing samples to each other.
The concept of hypotehsis testing deals with comparisons of sample statstics to another sample or to an actual value. For example, I may want to find out if male test scores are actually equal to female test scores. Using data from a sample, it is highly unlikely that the values would be exactly the same, however are they close enough to be considered “equal” or equal enough? Hypothesis testing deals with setting up a claim regarding the data that is being compared and then testing to see if this claim is supported by the data you are reviewing or not.
Statistical hypothesis statementns involve two hypothesis statements. The null hypothesis assumes there is no difference or effect in the data (each group is equal). While the alternative hypothesis states that the value or groups are different in some way. After stating the hypothesis, Excel will be used to determine which one the data supports.
You will find the same format as in last week’s Try This! document. In each Green Tab, you will find information about the task you should complete. In each Red Tab, you will find the solutions.
This process will permit you to check your work and ensure that you can complete the necessary exercises to learn the concept you are required to use for your IP.
If your solution does not agree with the “answer”provided, please feel free to contact me at
lemurray@faculty.aiuonline.edu
PLEASE NOTE: This resource is ONLY applicable to those students who use Microsoft Excel. Whether you use Microsoft or have a Mac, you still need to activate the processing software, such as the Data Analysis Toolpak or the StatPlus from AnalystSoft. Please review the information from the link in the Unit
1
Sample Data
Animal type
Visit Code
Gender
Weight (lbs)
Age (years)
Cost
Key
0
12
3
$12
5
Animal Type
Cat
0 2 0
14.3
$
8
Dog
0 1 1
8.
9
$109
Bird
0 1 0
7.3
0.5
$99
Other
0 3 1
10.3
$250
Routine check up
1 1 1
1
2.3
$150
Illness
1 2 0
78.2
$212
Emergency
1 2 1
50.4
$73
Male
1 1 1
33.2
$90
Female
1 3 0
89
$459
1 1 0
5.4
$153
2 1 1 2.3 2 $90
2 1 1
3.1
$85
3 2 1
3.4
$74
2 1 0
2.2
$48
1 2 1
102.3
$
6
1 2 1
32.1
1 1 0
67.2
$88
2 1 0 3.4 4
$40
2 2 1
1.2
$72
1 3 1
8.9
$280
SAMPLE DATA WARNING
This is the sample data set you will be using to demonstrate several concepts for the practice exercises. You will NOT be using this data set for any of your IPs in the course. This is for practice ONLY! For the course IPs, you will be using the data set that is available in the Unit 1 IP Assignment List, located via a link titled 2003 Excel Data Set and Data Set Key. Remember that you should use ONLY the current DataSet for the current course for the IP assignments. This sample data will be used for the “Try This” worksheet assignments for practice exercises. These are opportunities for you to learn the process before you complete your actual IP work. This is NOT a requirement. It is simply an opportunity to improve by learning the steps.
Sample Data Information
The data was collected one day from a veterinarian office. Twenty-one animals entered the practice for treatment of some kind. The Animal Type, Reason for Visit, Gender, Weight, Age and Cost of Visit were recorded. For ease of analysis, the first three variables were given a code (computers deal better with numbers). To assist you in the analysis, the qualitative variables’ labels were colored in RED. The quantitative variables’ labels were colored in GREEN.
Hypothesis Test
Before you can begin your hypothesis work you must clearly state the null and alternative hypothesis.
In this case, the vetrenarian would like to see if the cost of male animal visits is the same as the cost of female animal visits or not.
Notice the vet did not make any specific claims such as male animal vists are greater than female animal visits or that female animal visits are lower than male animal visits. Therefore, based on the way that we are looking at the comparison, we will make the test a two-tailed test.
The null and alternative statementns will look like this:
***Please note there is a sample test in the instructor files. You may use this as the basis for your hypothesis work by copying and pasting the test and changing the values as appropriate.***
If you would like to learn how to type these symbols out in Microsoft Word this video will assist you:
Typing hypothesis statements
Now that your statements are typed out it is time to perform the actual test! You need to use Excel to complete this task.
This video will assist you:
Hypothesis testing in excel
Now you are ready to try this with the sample data!
Be sure that your test includes the following: 1. Statements, 2. Alpha values, 3. Output from Excel, 4. Conclusion made including an explanation of WHY that decision was made and 5. An explanation as to what that decision means in the practical sense.
1.
Hypothesis Statements
2. Alpha values,
3. Output from Excel,
4. Conclusion made including an explanation of WHY that decision was made,
5. An explanation as to what that decision means in the practical sense.
Hypothesis Solution
Step One
The first thing you must do is sort out the data so all the female costs and male costs are in separate columns, you can do this by doing a sort, in the data tab you want to sort the data based on the variable you are interested in, in this case gender.
The sorted data should look like this:
MALES
FEMALES
$125
$85 $250
$99 $150
$212 $73
$459 $90
$153 $90
$48 $85
$88 $74
$40
$67
$109
$72
$280
Step 2
Next, you want the output from the test.
t-Test: Two-Sample Assuming Unequal
Variance
It is important to enter in a zero in the Hypothesized
Mean
Males
Females
Mean
145.4444
120.75
Variance
16
5099.841
Observations
Hypothesized Mean Difference
df
t Stat
0.518139
P(T<=t) one-tail
0.30689
t Critical one-tail
1.782288
P(T<=t) two-tail
0.613781
t Critical two-tail
2.178813
When you do your formal write up
Be sure your test includes the following:
1. Statements,
3. Output from Excel,
4. Conclusion made including an explanation of WHY that decision was made and
Final test write up
So in our case, for a two tailed test, the hypothesis statements and test would be as follows:
t-Test: Two-Sample Assuming Unequal Variances
Mean 145.4444 120.75
Variance 16618.28 5099.841
Observations 9 12
Hypothesized Mean Difference 0
df 12
t Stat 0.518139
P(T<=t) one-tail 0.30689
t Critical one-tail 1.782288
P(T<=t) two-tail 0.613781
t Critical two-tail 2.178813
Important numbers:
Significance level:
0.05
T stat
Critical T values -2.17 and 2.17 (remember a two tailed test has two values a positive and negative)
P value
YOU MAY DECIDE TO BASE YOUR DECISION ON A COMPARISON OF CRITICAL VALUES TO t-STAT VALUES
OR
If you decide to base your decision on a comparison of critical values to t-stat values:
Since the test statistic of 0.52 is less than the critical value of 2.18, then we fail to reject the null hypothesis statement (Ho).
If you decide to base your decision on a comparison of p values to alpha values:
Since the p value of 0.6 is greater than the alpha level of 0.05, we fail to reject the null hypothesis statement (Ho).
Either way, the decision is the same, and we must explain what it represents:
The data supports Ho, which states that there is NO significant difference between the cost for male and female animals.
Example 2
Now that you are getting good at this, let’s try another one. Suppose the veterinarian wants to test the hypothesis that weights are the same for male and female animals
You try this one yourself!!
Some things to think about
What variable are you sorting by?
What variable are you focusing on for the statements?
Example 2 Solution
Male Female
Mean
31.03333333
26.79166667
Variance
1292.3875
1045.840833
Hypothesized Mean Difference 0 df 16
t Stat
0.279231099
P(T<=t) one-tail
0.391822323
t Critical one-tail
1.745883676
P(T<=t) two-tail
0.78
t Critical two-tail
2.119905299
Signficance level
T stat 0.78
Critical values
–
2.12
2.12
P value 0.78
The test stat of 0.279 is in between the two critical values of -2.12 and 2.12
Therefore we fail to reject Ho
OR
The p value of 0.78 is larger than the alpha level of 0.05
The average weight of male and female animals can be considered to NOT be signiticantly different.
**A special note about p values
Sometimes excel cannot display very small numbers correctly. If your p value has an E in it at the end of the numbers, for example .78983E-5, this is a VERY small number and is presented in what is known as scientific notation format. This is essentially the same as a zero. That is how small it really is. It is another way of writing the number .0000078983. Any p-value with the E as part of the number may be considered to be a zero.
Your IP Checklist
HIGH FIVE!!
You have completed all sample exercises and are now ready to start your IP.
To help you get started, here is a checklist of items that need to be completed.
Completed?
Area
Download current session data set
This is the same data set from unit 1
Download template for IP
Your completed project should all be on this template. You are not required to submit your Excel file, but you should copy/paste all information from Excel onto the template.
Failure to use this template will result in an automatic 10-point penalty. DO NOT remove the bold headings.
State hypothesis statements for testing Intrinsic Satisfaction Values by Gender
Write an Ho and H1
You may find a sample to model your test after in the instructor files. You may download this and copy/paste it. Change the parts that you need to for the situation
A copy is also located here:
http://cl.ly/3Z1w0v3c473I1Q1j2d2A
For more information about typing out these statements please view:
http://proflmurray.webs.com/apps/videos/videos/show/14044874-typing-hypothesis-statements
Cited
http://cl.ly/3Z1w0v3c473I1Q1j2d2A
http://proflmurray.webs.com/apps/videos/videos/show/14044874-typing-hypothesis-statements
http://proflmurray.webs.com/apps/videos/videos/show/14044877-performing-a-hypothesis-statement-in-excel
DO NOT MAKE THESE ERRORS
| Learn from past students, don’t make these mistakes this unit!! |
| Discussion Board |
| Research |
| Be sure that your research is from a scholarly article from the library |
| -Cite your sources in APA format |
| Ethical Considerations |
| Your study should involve subjects (human or animal). Be sure to address how these subjects were protected. See the first page of the Unit 4 Newsletter for details. |
| Assess the Study |
| BE SPECIFIC as to whether there are any reasons to believe this is not well done. You may want to look to determine whether there any reasons to suspect the study is biased.How can you relate that to what you have learned so far in the course? |
| Interactive Replies |
| Be sure you reply in the required manner as per the guidelines in the assignment description. |
| Individual project |
| Be sure you have clearly defined Ho and H1. You need two statements |
| Your test should be two-tailed and that should be reflected in the hypothesis statements. |
| This test should compare males/females in the hourly/salaried categories. |
| Excel Output |
| Be sure to use a t-test assuming unequal variances. |
| Identify Key Values |
| Be sure to identify key values from this output specifically including the significance level, t-statistic, t-critical value, p value and alpha (significance value). |
| Explain Your Decision |
| It is important to not only make a decision as to which hypothesis supports your situation, but also explain why you made that decision. Relate this to the numbers. |
| Application for Managers |
| Be specific as to why your boss might be interested in these results. |
| Be careful not to make conclusions that the data cannot support. You cannot say that males=females in terms of work hours if we are comparing satisfaction levels, etc. |
| z-test vs. t-test |
| Be specific and avoid direct quotations: |
| Samples v populations |
| Do not just tell me what a sample is. Be sure to explain WHY we use them. |
| Cite references in the last page and in-text in the body of the document in APA format |
