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Task 1 linear - Passed
Data-Driven Decision Making (Western Governors University)
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TASK ONE LINEAR REGRESSION
LeeAnn Woodmancy
Western Governors University Downloaded by Ta keys (tokegb1@wgu.edu)
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Task 1: Linear Regression Analysis Scenario
Nurses and hospital staff are known for working numerous hours with little rest. Most shifts are twelve hours long caring for patients will all different ailments. A major hospital has decided
to deal with these issues and tackle the attrition level of nursing staff at their hospital. In order to
determine if they want to invest in these programs the hospital has compiled data over the last 36
months. The data that we are presented with is the attrition rate vs. program participation rate. We will analyze the data and determine if the programs are working or if the data shows that there is no relationship between the two elements. A business question that could be answered by applying linear regression analysis would be, by providing an
employee well-being program, will the turnover rate be reduced? There are many things we can look at when we analyze the data regarding turnover rates among nurses at a major hospital. The independent variable in the scenario would be the program participation rate. The reason that it is independent is that it can stand on its own and does not require another variable to make it work. The dependent variable would be the Nurse attrition rate. This is dependent as it requires other factors to determine this rate. The type of data is ratio/ordinal data. Percentages are ratio data as they are in a set order, or scale. The quantity of data is quantitive. It Quantifies the problem using numbers. Quantitive also deals with measurements and analytics. The data that was collected was for a total of 36 months. This
data includes the number of nurses that attended the programs and the attrition for each month. 1
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The technique that I used was Scatter Plot. This analysis is ideal for studying the data between two subjects. In this case, we are looking at if nurses attend the programs for well-
being, the attrition rate would decrease. I feel that this is a good study as it will give me information showing me if the program works or if there is no relation between the two. Linear regression is the appropriate analysis technique for predicting the dependent variable because it allows us to analyze between an independent and a dependent variable. The independent variable would be the participants in the study and the dependent would be the nurse
attrition rate. Using linear regression allows us to identify risk factors and calculate those scores for use in determining how to continue to help lower the attrition level. SUMMARY OUTPUT
Regression Statistics
Multiple R
0.74428486
R Square
0.55395995
2
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Adjusted R Square
0.54084112
Standard Error
0.82520629
Observations
36
ANOVA
df
SS
MS
F
Regression
1
28.7546759
28.75468
42.22634
Residual
34
23.1528241
0.680965
Total
35
51.9075
Coefficients
Standard Error
t Stat
P-value
Intercept
5.58955642
0.37464865
14.91946
1.74E-16
Program Participation Rate (%)
-0.0849386
0.01307113
-6.49818
1.96E-07
As we look at the calculations, the p-value or ANOVA result is a statistical relationship shown as p<0.05. If it is this result, then that shows a null hypothesis is rejected and therefore there is no relationship. This data shows a P-value of 1.96E-07 which indicates that the null hypotheses will be rejected. The null hypothesis would state that there is no significant relationship between attending a program and the attrition rate. Using the data provided we could
ask is there a significant relationship between Attrition Rate, x, and program participation percentage. This analysis states that there are direct relationships or trends that indicate that attending these programs influences the attrition rate. Based on the linear regression results, we can see that the trend is that attrition lowers as more nurses attend the programs. This means that
the programs are working and that the attrition levels are dropping. The equation used to obtain this data's results obtain the results of this data is y= -0.0849x+5.5896
. When looking at data, we also need to consider the research limitations that could affect a recommended course of action. Because the relationship is significant, the equation can be used for future work to determine how the program is still affecting attrition. We know that based on the p-value there 3
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is a significant relationship. When looking at the data we cannot determine the ages of the nurses, nor if they have any medical issues or family issues that would cause the results to be askew. When we look at the goodness of fit, we see that it is 0.554. As we know 0.554 is moderate. If the R-square is closer to 0 it is not a good fit. If the R-square is a 1 then it is a perfect fit. the goodness of fit will help determine if this data could be skewed or if it’s a representation we would expect. We also need to take into consideration that this data does not tell us if these nurses are continuing with the programs or how many times they have attended. Because of this missing information, we cannot be certain that the attrition level will continue to lower because of unforeseen problems. Based on the data that was analyzed, I would recommend the hospital continue with its plan of funding the program for the next five years. Based on the information, the attrition rate is
dropping consistently as the nurses are attending the programs. If additional information was available, a more definite conclusion could be formed. 4
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Chapter 3, Section 2, Exercise 06laca
ASSIGNMENT
Have You Ever Been Arrested?
RESOURCES
Wiley 00 - Some more
practice probs for
Exam 2
M Chapter 3, Section 1,
Exercise 024 -
MathPad
According to a recent study of 7335 young people in the US, 30% had been arrested' for a crime other than a traffic violation by the age of 23. Crimes included such things as vandalism, underage drinking,
drunken driving, shoplifting, and drug possession.
From a study in USA Today, quoted in The Week, 2012; 11: 547-548.
M Chapter 3, Section 1,
Exercise 026abc
Chapter 3, Section 1
Exercise 027 -
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M Chapter 3, Section 1,
Exercise 028 -
MathPad
Correct.
(a) Is the 30% a parameter or a statistic?
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heliumfootballs.txt
StatCrunch
Applets
Edit -
Data -
Stat
Graph Help-
Row
Distances of air Distances of he
var3
var4
var5
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234 5679 o
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Chapter P, Section 2, Exercise 062
Identifying Spam Text Messages
Bayes' rule can be used to identify and filter spam emails and text messages. This question refers to a large collection of real SMS text messages from participating cellphone users.' In this collection,
747 of the 5574 total messages (13.40%) are identified as spam. The word "free" is contained in 4.75% of all messages, and 3.57% of all messages both contain the word "free" and are marked as
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Taiwan
Panama
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Real GDP per Capita in
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$1,861
2,120
2,483
4,105
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10,132
11,204
14,263
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2.45
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3.52
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2.54
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L
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Section Read
Community
Type
New
Spor
Com
S
ts
ics
Total
City
171
100
89
360
2.30
0.59
1.22
Chi_sq
13
26
67
Suburb
121
111
99
331
2.68
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7
Rural
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89
90
310
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300
278
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Use the data to construct a model that predicts the tax assessment value assigned to medical office buildings with specific characteristics.
Construct a scatter plot in Excel with FloorArea as the independent variable and AssessmentValue as the dependent variable. Insert the bivariate linear regression equation and r^2 in your graph. Do you observe a linear relationship between the 2 variables?
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Brand A, Brand B, and Brand C sold a number of items each month in 2019. Each brand described their item sales in the box plots shown below. For which
of these brands would you expect that the mean would be less than the median?
Brand C
Brand B
Brand A
500
1000
1500
2000
Brand A and Brand B
Brand B and Brand C
O Brand A and Brand C
O None of these box plots suggest that the mean would be less than the median.
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