Running head: DATA ANALYSIS USING DESCRIPTIVE STATISTICS
Data Analysis Using Descriptive Statistics
Marissa Navar
University of Phoenix
Research and Evaluatiion I
RES341
Richard A. Stanley
June 28, 2009
Data Analysis Using Descriptive Statistics
Histogram
The Histogram chart shows the measurement of frequency in home buyers. It shows what home buyers are willing to spend in today’s current economy. The histogram explains although the economy is in a bad state that some home buyers are not willing to go under or above their original price range no matter what special features they are looking for.
In our research we wanted to know what home buyers were looking for when purchasing a home or what were in some of the homes that were selling in the economy at today’s state. We believed that it’s important to understand and know what buyers are looking for and what’s important to them for the sake of the economy. The histogram shows the median in home buyers where the frequency is high. This means that at the frequency of 11 home buyers are willing to spend 233550 on a home, although the chart does not show the exact things home buyers are looking for when purchasing a home it’s obvious at 233550, whatever comes with a home in such areas, this is what most individuals are looking for. It can also be argued that at a lower frequency many home buyers are not willing to spend in the price ranges of 139900 and 186725 and it is safe to say that it doesn’t matter what features comes with these homes its obviously not the heightened picks for home buyers.
Conclusion
With all of our research and data analysis we did get an answer for our problem statement but at the same time we believe it is a problem statement that can always have more research. We did get enough data so that we can have enough information to put our data together and investigate what buyers want and what they look for when they are looking for a home. This information...
...hypothesis testing of two samples that had two means. The reason why the choice of testing of two samples means were because it compared the two sets of data that are directly related to each other. The reason why I believed that rural homes have a lower average of beds due to the fact that rural areas are the countryside rather than the big known towns or towns of the state.
The population that my data set represents was the number of beds that the inpatients had in each of the homes between nonrural home and rural home facilities. The reason why the data was collected was because the Department of Health and Social Services of the State of New Mexico and cover 60 licensed nursing facilities in New Mexico in 1988. The methods that were used to collect the data was by the number of beds that were used in the home, annual medical in patient days (hundreds), annual total patient days (hundreds), annual total patient care revenue ($hundreds), annual nursing salaries ($hundreds), annual facilities expenditures ($hundred), and where the home was located between nonrural and rural areas. The source of the data set of the nursing home information toward New Mexico in 1988 was part of the data analyzed by Howard L. Smith, Niell F. Piland, and Nancy Fisher. This was published in the Journal of Rural Health in winter 1992. This data set can be calculated in four different types of...
...
DataAnalysis
Descriptive Statistics, Estimation, Regression & Correlation
Treatment Effects of a Drug on Cognitive Functioning in Children with Mental Retardation and ADHD
Hossam Elhowary
MATH101615
Dr. Maria DeLucia
December 09, 2014
Introduction
The purpose of this survey was to investigate the cognitive effects of stimulant medication in children with mental retardation and AttentionDeficit/Hyperactivity Disorder. Twenty four children were given various dosage of a drug a placebo and 0.60mg/kg. Variable descriptions are kind of drug taken and the number of correct responses after taking of the drug. They were on each dose one week before testing. This sample obtained from the preschool delay task of Gordon Diagnostic System (Gordon, 1983). However, does higher dosage lead to higher cognitive performance?
Histogram:
Boxandwhisker plot:
Multi plot:
Summary statistics:
Column
n
Mean
Variance
Std. dev.
Std. err.
Median
Range
Min
Max
Q1
Q3
Placebo
24
39.75
128.02174
11.314669
2.3095972
36
45
26
71
33
47
0.60
24
44.708333
151.7808
12.319935
2.5147962
42.5
48
29
77
35
54
Simple linear regression results:
Dependent Variable: .60 mg/kg
Independent Variable: Placebo
.60 mg/kg = 10.091611 + 0.87086093 Placebo
Sample size: 24
R (correlation coefficient) = 0.79980157
Rsq = 0.63968255
Estimate of error standard deviation: 7.5614248
Parameter estimates:
Parameter
Estimate
Std. Err.
Alternative...
...
Simply use statistics as a tool. You will be given a data. (Next year you will not be given data, you will gather data yoruself).
1. Data: one of the variables is dependent and other dependent. Can be multiple. Then do regression analysis. ANOVA for overall significance and Regression equation. And write based on ANOVA there is a significance or not.
2. Some comments on correlation: volume vs. horse power etc.
3. Hypothesis test of one population. I assume that the mean is etc etc. Small paragraph analysis below the results of the test. ANOVA for small, large and medium size businesses for example.
Simply use statistics as a tool. You will be given a data. (Next year you will not be given data, you will gather data yoruself).
1. Data: one of the variables is dependent and other dependent. Can be multiple. Then do regression analysis. ANOVA for overall significance and Regression equation. And write based on ANOVA there is a significance or not.
2. Some comments on correlation: volume vs. horse power etc.
3. Hypothesis test of one population. I assume that the mean is etc etc. Small paragraph analysis below the results of the test. ANOVA for small, large and medium size businesses for example.
Simply use statistics as a tool. You will be...
...,
STATISTICS "FOR
BUSINESS AND
ECONOMICS ~
SECOND EDITION
SWEENEY WILLIAMS
ANDERSON
FREEMAN
~...
t
SHOESMITH
SOUTHWESTERN
(ENGAGE Learning'
Australia· Brazil· Japan· Korea· Mexico· Singapore. Spain. United Kingdom. United States
Brief contents
Preface and Acknowledgements xvii
About the Authors xx
Walkthrough Tour xxii
Accompanying Website xxiv
Supplements xxv
Data and Statistics
I
2 DescriptiveStatistics: Tabular and Graphical Presentations
3 Descriptive Statistics: Numerical Measures
4 Introduction to Probability
21
67
117
5 Discrete Probability Distributions 153
6 Continuous Probability Distributions
187
7 Sampling and Sampling Distributions 219
8 Interval Estimation
9 Hypothesis Tests
251
283
10 Statistical Inference about Means and Proportions with Two
Populations
335
I I Inferences about Population Variances
373
12 Tests of Goodness of Fit and Independence 399
13 Analysis of Variance and Experimental Design 429
14 Simple linear Regression 489
15 Multiple Regression 555
16 Regression Analysis: Model Building 613
17 Index Numbers 659
18 Forecasting 683
ix
,
BRIEF CONTENTS
19 Nonparametric Methods
727
20 Statistical Methods for Quality Control
21 Decision Analysis
799
22 Sample Surveys (on CD)
Appendix A References and...
...and Analyzing Data
Collecting and Analyzing Data

 Contributed by Phil Rabinowitz and Stephen FawcettEdited by Christina Holt 
What do we mean by collecting data?
What do we mean by analyzing data?
Why should you collect and analyze data for your evaluation?
When and by whom should data be collected and analyzed?
How do you collect and analyze data?
In previous sections of this chapter, we’ve discussed studying the issue, deciding on a research design, and creating an observational system for gathering information for your evaluation. Now it’s time to collect your data and analyze it – figuring out what it means – so that you can use it to draw some conclusions about your work. In this section, we’ll examine how to do just that.
What do we mean by collecting data?
Essentially, collecting data means putting your design for collecting information into operation. You’ve decided how you’re going to get information – whether by direct observation, interviews, surveys, experiments and testing, or other methods – and now you and/or other observers have to implement your plan. There’s a bit more to collecting data, however. If you are conducting observations, for example, you’ll have to define what you’re observing and arrange to make observations at the right times, so you actually observe what...
...H₁: μ>94.4
Rejection Region:
Degree of freedom:
d.f=n1
=49
t> ta,d.f
t>0.05,49
t>1.6766
Test statistics:
t=
From using the DataAnalysis Plus in Excel we get:
tTest: Mean
Cleanser Spending
Mean
102.4000
Standard Deviation
27.5711
Hypothesized Mean
94.4
df
49.0000
t Stat
2.0517
P(T1.6766).
2)
± ta/2,d.f s/
From using the DataAnalysis Plus in Excel we get:
tEstimate:Mean
Cleanser Spending
Mean
102.4000
Standard Deviation
27.5711
LCL
94.5645
UCL
110.2355
We estimate that the mean amount spent over one year lies between $94.56 and $110.26and when we divided by 4 we get the mean amount spent for every 3 month:
We estimate that the mean amount spent for every 3 month lies between $23.64 and $27.56. This estimate is 95% correct of the time
Case 2:
1)
Hypotheses:
H₀: μ=142
H₁: μ≠ 142
Rejection Region:
Degree of freedom:
d.f= n1
=23
tta/2,d.f
tt0.05,23
t17139
Test statistic:
t=
From using the DataAnalysis Plus in Excel we get:
tTest: Mean
Hot Chocolate
Mean
141.3750
Standard Deviation
1.9959
Hypothesized Mean
142
df
23
t Stat
1.5341
P(T9
Rejection Region:
Degree of freedom:
d.f= n1
= 23
χ² > χ²a,d.f
χ² > χ² 0.1, 23
χ² > 32.0069
Test statistic:
χ²=
From using the...
...DataAnalysis Report
Case Study – Computers R Us
Executive summary
This report aims to figure out two basic questions, current consumers’ satisfaction and strategy that would be most potent to increase overall satisfaction. At the beginning, a survey of three parts was designed and conducted across different age groups.420 samples were collected and coding and editing of a variety of data ensued. By resorting to hypothesis test, regression model, this report found out that current consumers’ satisfaction level is much lower than management expected and two gender groups show significantly different satisfaction .Also, consumer satisfaction was similar across five age groups. In respect of determinants of consumer satisfaction, satisfaction with response time, satisfaction with the level of advice from staff at call centre, satisfaction with the level of communication and satisfaction with loyalty rewards program from consumers are all key contributors while only satisfaction with the level of communication has positive relationship with overall consumer satisfaction. Thus, in order to make effective strategy to improve overall satisfaction, Computer R Us should increase methods of communication.
Introduction
Computer R Us, whose main business focuses on manufacture and retail of computers, recently set up a division called Completecare. This division offers consumers service and repair for its computers. However, in its everyday...