At the request of and in cooperation with the City Fire & Police Commission, in 2014, the
Center for Urban Initiatives and Research (CUIR) conducted the City Police Satisfaction
Survey. The purpose of this survey was to measure resident perceptions regarding a range
of issues relevant to the Police Department; satisfaction with and trust in the police,
perceptions of safety and police visibility, views on various kinds of police contacts, and
exposure to crime. The survey was structured to provide estimates of both city-wide
opinion as well as estimates of opinion within each police district.
This RDD telephone survey was conducted by CUIR Survey Center staff from July 17th
through August 23rd. Surveys were conducted in both English and Spanish. Of the 1,452
completed interviews, 729 (46.2%) were over landlines, while 781 (53.8%) were over
mobile lines. The margin of error for unweighted sample statistics is ±2.6% at the 95%
confidence level.

Contact us for coaching on case studies and understanding of confidence interval estimates using

software such as PH Stats or Stat Tools


AJ Fitness

Project 1:   Getting to know your data

Please feel free to impress by properly using recently learned statistical terminology when appropriate.

1.  Explain in detail what your data entail.  Include why you think the data were collected and how the data may be used by the party who collected them. Also, who is the target audience?

2.  Are the data from a sample or a population?  If they are a sample, what or who is the target population?

3.  List the quantitative and qualitive variables.

4.  Using Excel, make a graph or table of one or more of your qualitative variables.  You will likely have to group the data together into categories.

5.  Determne any patterns, trends or possible problems from your graph or chart from question

6.  Is there additoinal data that you would have collected?  If so, what and why?

Project 2 .

1. Choose three (3) of your quantitative variables in your data set and caculate the following:  Mean, Median, Mode, Range, Standard Deviation and IQR.

2. What are you hoping to learn that made you chooose those three variables?

3. Based on your answers to question 1, discuss the whether the variables are symmetric or skewed.

4. Use one of the methods discussed in class to determine if any of your variables contain outliers.  If so, list the outliers.

5. Based on your answers to questions 3 & 4, which measure of center best describes each variable.

6. Calculate the coefficient of variation for each variable to and rank them from least to greatest in terms of relative variability.  Is this what your expected-explain.


Mock Problems

Statistic Pre – requisite Sample Question

1)      What is the underlying element of all statistical sampling techniques?


2)      As a member of the student council at your university, you have been assigned the task of conducting a phone survey of undergraduate students to determine satisfaction with the campus food service. Explain how you would go about selecting a simple random sample?


3)      Explain the difference between a stratified random sample and cluster random sample


4)      Explain the types of information that can be conveyed by a frequency histogram


5)      Why would a histogram contain no gaps between the bars but a bar chat may have gaps?


6)      Suppose that you have a data set of 512 observations and the data value range from 36 to 187. What classes would you choose for this data set? Explain why you would choose these values.


7)      The AMI Company has two assembly lines in its Kansas City plant. Line A produces an average of 335 units per day with a standard deviation equal to 11 units. Line B produces an average of 145 units per day with a standard deviation equal to 8 units. Based on the information, which line is relatively more consistent?


8)      The following sample data reflect electricity bills for ten households in San Diego in March.


$118.20 $67.88 $133.40 $88.42 $110.34
$76.90 $144.56 $127.89 $89.34 $129.10

Determine three measures of central tendency for these sample data. Then, based on these measures, explain why you think the sample data are symmetric or skewed.


9)      Explain how the empirical rule can be used to help describe data in a population or a sample


10)   Until the summer of 2006, the real estate market in Fresno, California, had been booming, with prices skyrocketing. Recently a study showed the sales patterns in Fresno for single-family homes. One chart presented in the commission’s report is reproduced here. It shows the number of homes sold by price range and number of days the home was on the market.



Days on the Market

Price Range ($000)



Over 30

Under $200












Over $1000





Construct and answer three questions from these data using:

a)      Addition rule

b)      The multiplication rule

c)       Conditional probability


11)   Explain what is meant by the term mutually exclusive events. Cite an example


12)   What is the difference between a discrete random variable and a continuous random variable? Give examples.


13)   Explain what the expected value of a discrete random variable measures.

Continuous probability distribution

The useful life of an electrical component is exponentially distributed with a mean of x hrs.

What is the probability the circuit will last more than 3000 hours?

To solve this you must first find the new mean or Lambda.

Exponentially distributed with a mean of 2500 means the

converted rate is 1/2500 or 0.0004

You may use a software such as PHStats to solve problem.

For assistance with this type question contact for a session.



Introductory statistics

Learn how to use PhStats , Excel or your TI calculator to zip through this course.

We can also show you how to calculate step by step and understand the course concepts.

Elementary Statistics

Introduction to Statistics



Statistical Thinking

Types of Data

Critical Thinking

Design of Experiments

Summarizing and Graphing Data



Frequency Distributions


Statistical Graphics

Critical Thinking: Bad Graphs

Statistics for Describing, Exploring, and Comparing Data



Measures of Center

Measures of Variation

Measures of Relative Standing and Boxplots





Addition Rule

Multiplication Rule: Basics

Multiplication Rule: Complements and Conditional Probability

Probabilities Through Simulations


Bayes’ Theorem (on CD-ROM)

Discrete Probability Distributions



Random Variables

Binomial Probability Distributions

Mean, Variance, and Standard Deviation for the Binomial Distribution

The Poisson Distribution

Normal Probability Distributions



The Standard Normal Distribution

Applications of Normal Distributions

Sampling Distributions and Estimators

The Central Limit Theorem

Normal as Approximation to Binomial

Assessing Normality

Estimates and Sample Sizes



Estimating at Population Proportion

Estimating Population Mean: ? Known

Estimating a Population Mean: ? Not Known

Estimating a Population Variance

Hypothesis Testing



Basics of Hypothesis Testing

Testing a Claim About a Proportion

Testing a Claim About a Mean: ? Known

Testing a Claim About a Mean: ? Not Known

Testing a Claim About Variation

Inferences from Two Samples



Inferences About Two Proportions

Inferences About Two Means: Independent Samples

Inferences from Matched Pairs

Comparing Variation in Two Samples

Correlation and Regression




Contingency Tables

Variation and Prediction Intervals

Multiple Regression


Goodness-of-Fit and Contingency Tables




Contingency Tables

McNemar’s Test for Matched Pairs

Analysis of Variance





Nonparametric Statistics



Sign Test

Wilcoxon Signed Ranks Test for Matched Pairs

Wilcoxon Ranked-Sum Test for Two Independent Samples

Kruskal-Wallis Test

Rank Correlation

Runs Test for Randomness

Statistics Process Control



Control Charts for Variation and Mean

Control Charts for Attributes


PH Stats

Business Statistics

Business Statistics / Intro Statistics

Topics tutored include:

  • The Where, Why, and How of Data Collection
  • Graphs, Charts, and Tables – Describing Your Data
  • Describing Data Using Numerical Measures
  • Using Probability and Probability Distributions
  • Discrete Probability Distributions
  • Introduction to Continuous Probability Distributions
  • Introduction to Sampling Distributions
  • Estimating Single Population Parameters
  • Introduction to Hypothesis Testing
  • Estimation and Hypothesis Testing for Two Population Parameters
  • Hypothesis Testing and Estimation for Population Variances
  • Analysis of Variance
  • Goodness-of-Fit Test and Contingency Analysis
  • Introduction to Linear and Correlation Analysis
  • Multiple Regression Analysis and Model Building
  • Analyzing and Forecasting Times Series Data
  • Introduction to Nonparametric Statistics
  • Introduction to Quality and Statistical Process Controls
  • Introduction to Decision Analysis

Most Popular Text Book

Business Statistics: A Decision Making Approach (7th Edition), Groebner/ Shannon/Fry/Smith

Business Statistics: Contemporary Decision Making (6th Edition), Ken Black

Statistics for Managers Using Microsoft Excel (5th Edition), Levine/Stephan/Krehbiel/Berenson