# I really need assistance with my BUS 308 week 4 Assignment. Any Assistance would be appreciated. The

I really need assistance with my BUS 308 week 4 Assignment. Any Assistance would be appreciated. The first question is a follows… Using our sample data, construct a 95% confidence interval for the population's mean salary for each gender. Interpret the results. How do they compare with the findings in the week 2 one sample t-test outcomes (Question 1). See comments at the right of the data set. ID Salary Compa Midpoint Age Performance Rating Service Gender Raise Degree Gender1 Grade 8 23 1.000 23 32 90 9 1 5.8 0 F A The ongoing question that the weekly assignments will focus on is: Are males and females paid the same for equal work (under the Equal Pay Act)? 10 22 0.956 23 30 80 7 1 4.7 0 F A Note: to simplfy the analysis, we will assume that jobs within each grade comprise equal work. 11 23 1.000 23 41 100 19 1 4.8 0 F A 14 24 1.043 23 32 90 12 1 6 0 F A The column labels in the table mean: 15 24 1.043 23 32 80 8 1 4.9 0 F A ID – Employee sample number Salary – Salary in thousands 23 23 1.000 23 36 65 6 1 3.3 1 F A Age – Age in years Performance Rating – Appraisal rating (Employee evaluation score) 26 24 1.043 23 22 95 2 1 6.2 1 F A Service – Years of service (rounded) Gender: 0 = male, 1 = female 31 24 1.043 23 29 60 4 1 3.9 0 F A Midpoint – salary grade midpoint Raise – percent of last raise 35 24 1.043 23 23 90 4 1 5.3 1 F A Grade – job/pay grade Degree (0= BSBA 1 = MS) 36 23 1.000 23 27 75 3 1 4.3 1 F A Gender1 (Male or Female) Compa – salary divided by midpoint 37 22 0.956 23 22 95 2 1 6.2 1 F A 42 24 1.043 23 32 100 8 1 5.7 0 F A 3 34 1.096 31 30 75 5 1 3.6 0 F B 18 36 1.161 31 31 80 11 1 5.6 1 F B 20 34 1.096 31 44 70 16 1 4.8 1 F B 39 35 1.129 31 27 90 6 1 5.5 1 F B 7 41 1.025 40 32 100 8 1 5.7 0 F C 13 42 1.050 40 30 100 2 1 4.7 1 F C 22 57 1.187 48 48 65 6 1 3.8 0 F D 24 50 1.041 48 30 75 9 1 3.8 1 F D 45 55 1.145 48 36 95 8 1 5.2 0 F D 17 69 1.210 57 27 55 3 1 3 0 F E 48 65 1.140 57 34 90 11 1 5.3 1 F E 28 75 1.119 67 44 95 9 1 4.4 1 F F 43 77 1.149 67 42 95 20 1 5.5 1 F F 19 24 1.043 23 32 85 1 0 4.6 1 M A 25 24 1.043 23 41 70 4 0 4 0 M A 40 25 1.086 23 24 90 2 0 6.3 0 M A 2 27 0.870 31 52 80 7 0 3.9 0 M B 32 28 0.903 31 25 95 4 0 5.6 0 M B 34 28 0.903 31 26 80 2 0 4.9 1 M B 16 47 1.175 40 44 90 4 0 5.7 0 M C 27 40 1.000 40 35 80 7 0 3.9 1 M C 41 43 1.075 40 25 80 5 0 4.3 0 M C 5 47 0.979 48 36 90 16 0 5.7 1 M D 30 49 1.020 48 45 90 18 0 4.3 0 M D 1 58 1.017 57 34 85 8 0 5.7 0 M E 4 66 1.157 57 42 100 16 0 5.5 1 M E 12 60 1.052 57 52 95 22 0 4.5 0 M E 33 64 1.122 57 35 90 9 0 5.5 1 M E 38 56 0.982 57 45 95 11 0 4.5 0 M E 44 60 1.052 57 45 90 16 0 5.2 1 M E 46 65 1.140 57 39 75 20 0 3.9 1 M E 47 62 1.087 57 37 95 5 0 5.5 1 M E 49 60 1.052 57 41 95 21 0 6.6 0 M E 50 66 1.157 57 38 80 12 0 4.6 0 M E 6 76 1.134 67 36 70 12 0 4.5 1 M F 9 77 1.149 67 49 100 10 0 4 1 M F 21 76 1.134 67 43 95 13 0 6.3 1 M F 29 72 1.074 67 52 95 5 0 5.4 0 M F Week 1. Measurement and Description – chapters 1 and 2 1 Measurement issues. Data, even numerically coded variables, can be one of 4 levels – nominal, ordinal, interval, or ratio. It is important to identify which level a variable is, as this impact the kind of analysis we can do with the data. For example, descriptive statistics such as means can only be done on interval or ratio level data. Please list under each label, the variables in our data set that belong in each group. Nominal Ordinal Interval Ratio b. For each variable that you did not call ratio, why did you make that decision? 2 The first step in analyzing data sets is to find some summary descriptive statistics for key variables. For salary, compa, age, performance rating, and service; find the mean, standard deviation, and range for 3 groups: overall sample, Females, and Males. You can use either the Data Analysis Descriptive Statistics tool or the Fx =average and =stdev functions. (the range must be found using the difference between the =max and =min functions with Fx) functions. Note: Place data to the right, if you use Descriptive statistics, place that to the right as well. Salary Compa Age Perf. Rat. Service Overall Mean Standard Deviation Range Female Mean Standard Deviation Range Male Mean Standard Deviation Range 3 What is the probability for a: Probability a. Randomly selected person being a male in grade E? b. Randomly selected male being in grade E? Note part b is the same as given a male, what is probabilty of being in grade E? c. Why are the results different? 4 For each group (overall, females, and males) find: Overall Female Male a. The value that cuts off the top 1/3 salary in each group. b. The z score for each value: c. The normal curve probability of exceeding this score: d. What is the empirical probability of being at or exceeding this salary value? e. The value that cuts off the top 1/3 compa in each group. f. The z score for each value: g. The normal curve probability of exceeding this score: h. What is the empirical probability of being at or exceeding this compa value? i. How do you interpret the relationship between the data sets? What do they mean about our equal pay for equal work question? 5. What conclusions can you make about the issue of male and female pay equality? Are all of the results consistent? What is the difference between the sal and compa measures of pay? Conclusions from looking at salary results: Conclusions from looking at compa results: Do both salary measures show the same results? Can we make any conclusions about equal pay for equal work yet? Week 2 Testing means Q3 In questions 2 and 3, be sure to include the null and alternate hypotheses you will be testing. Ho Female Male Female In the first 3 questions use alpha = 0.05 in making your decisions on rejecting or not rejecting the null hypothesis. 45 34 1.017 1.096 45 41 0.870 1.025 1 Below are 2 one-sample t-tests comparing male and female average salaries to the overall sample mean. 45 23 1.157 1.000 (Note: a one-sample t-test in Excel can be performed by selecting the 2-sample unequal variance t-test and making the second variable = Ho value — see column S) 45 22 0.979 0.956 Based on our sample, how do you interpret the results and what do these results suggest about the population means for male and female average salaries? 45 23 1.134 1.000 Males Females 45 42 1.149 1.050 Ho: Mean salary = 45 Ho: Mean salary = 45 45 24 1.052 1.043 Ha: Mean salary =/= 45 Ha: Mean salary =/= 45 45 24 1.175 1.043 45 69 1.043 1.210 Note: While the results both below are actually from Excel's t-Test: Two-Sample Assuming Unequal Variances, 45 36 1.134 1.161 having no variance in the Ho variable makes the calculations default to the one-sample t-test outcome – we are tricking Excel into doing a one sample test for us. 45 34 1.043 1.096 Male Ho Female Ho 45 57 1.000 1.187 Mean 52 45 Mean 38 45 45 23 1.074 1.000 Variance 316 0 Variance 334.6666667 0 45 50 1.020 1.041 Observations 25 25 Observations 25 25 45 24 0.903 1.043 Hypothesized Mean Difference 0 Hypothesized Mean Difference 0 45 75 1.122 1.119 df 24 df 24 45 24 0.903 1.043 t Stat 1.968903827 t Stat -1.913206357 45 24 0.982 1.043 P(T<=t) > 0.05? Is P-value > 0.05? 45 65 1.157 1.140 Why do we not reject Ho? Why do we not reject Ho? Interpretation: 2 Based on our sample data set, perform a 2-sample t-test to see if the population male and female average salaries could be equal to each other. (Since we have not yet covered testing for variance equality, assume the data sets have statistically equal variances.) Ho: Ha: Test to use: Place B43 in Outcome range box. P-value is: Is P-value < 0.05? Reject or do not reject Ho: If the null hypothesis was rejected, what is the effect size value: Meaning of effect size measure: Interpretation: b. Since the > Interpretation: 2 Using our sample data, construct a 95% confidence interval for the mean salary difference between the genders in the population. How does this compare to the findings in week 2, question 2? Difference St Err. T value Low to High Yes/No Can the means be equal? Why? How does this compare to the week 2, question 2 result (2 sampe t-test)? a. Why is using a two sample tool (t-test, confidence interval) a better choice than using 2 one-sample techniques when comparing two samples? 3 We found last week that the degrees compa values within the population. do not impact compa rates. This does not mean that degrees are distributed evenly across the grades and genders. Do males and females have athe same distribution of degrees by grade? (Note: while technically the sample size might not be large enough to perform this test, ignore this limitation for this exercise.) What are the hypothesis statements: Ho: Ha: Note: You can either use the Excel Chi-related functions or do the calculations manually. Data input tables – graduate degrees by gender and grade level OBSERVED A B C D E F Total Do manual calculations per cell here (if desired) M Grad A B C D E F Fem Grad M Grad Male Und Fem Grad Female Und Male Und Female Und Sum = EXPECTED M Grad For this exercise – ignore the requirement for a correction Fem Grad for expected values less than 5. Male Und Female Und Interpretation: What is the value of the chi square statistic: What is the p-value associated with this value: Is the p-value