BUSM70011
Statistics
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  I: Administrative Information   II: Course Details   III: Topical Outline(s)  Printable Version
 
Section I: Administrative Information
  Total hours: 56.0
Credit Value: 4.0
Credit Value Notes: N/A
Effective: Spring/Summer 2016
Prerequisites: N/A
Corequisites: N/A
Equivalents:
N/A
Pre/Co/Equiv Notes: N/A

Program(s): N/A
Program Coordinator(s): N/A
Course Leader or Contact: N/A
Version:
1.0
Status: Approved (APPR)

Section I Notes: N/A

 
 
Section II: Course Details

Detailed Description
Introduction to Statistics; Organizing and Summarizing Univariate Data; Summarizing the Relationships between Variables: Regression and Correlation; Probability and Probability Distributions; Sampling and Sampling Distributions; Inferences for One Proportion; Inferences for One Mean; Statistical Process Control; Chi-Square Analysis; Regression Analysis;

Course Critical Performance and Learning Outcomes

 
 1. Create a report which summarizes numerical data in a form from 
which business decisions can be
made.

2. Predict (estimate) future or current values for a variable based 
on current data, using both manual
calculations and computer software.

3. Incorporate the use of probability and risk into decision making.

4. Integrate probabilities into estimating population parameters 
based on sample statistics.

5. Use statistical techniques for estimating population means and 
proportions.

6. Use statistical techniques to determine if a current process or 
belief is true based on sample data.

7. Use statistical process control techniques to determine if a 
process is running within acceptable
industrial standards.

8. Use statistical techniques to test categorical and/or multiple 
independent distributions.

9. Determine if linear regression is a robust model for predicting a 
value.
Evaluation Plan
Students demonstrate their learning in the following ways:

 
Assignment(s) 10%
Lab(s) 15%
Mid Term Exam 35%
Final Exam 40%
Total 100%
Provincial Context
The course meets the following Ministry of Training, Colleges and Universities requirements:

 

Essential Employability Skills
Essential Employability Skills emphasized in the course:

X Communication   Critical Thinking & Problem Solving   Interpersonal
  Numeracy X Information Management   Personal

Notes: N/A

Prior Learning Assessment and Recognition
PLAR Contact (if course is PLAR-eligible) : Office of the Registrar

Students may apply to receive credit by demonstrating achievement of the course learning outcomes through previous relevant work/life experience, service, self-study and training on the job. This course is eligible for challenge through the following method(s):

Challenge Exam Portfolio Interview Other Not Eligible for PLAR
         

Notes:  N/A

 
 
Section III: Topical Outline
Some details of this outline may change as a result of circumstances such as weather cancellations, College and student activities, and class timetabling.
Effective term: Spring/Summer 2016
Professor: Multiple Professors
Textbook(s):
Title: Stats Data and Models with My StatLab
Publisher: Pearson, 3rd Edition
ISBN: 9780132859547
Computer and Internet access required

Applicable student group(s): Faculty of Continuing and Professional Studies students
Course Details:
1. Create a report which summarizes numerical data in a form from 
which business decisions can be
made.

Construct pareto chart, pie chart, pictograph, stem and leaf, 
histogram.
Interpret pareto chart, pie chart, pictograph, stem and leaf, 
histogram.
Identify shape of curve for univariate data.
Calculate numerical measures of centre and variation.
Identify correct measure of centre based on the shape of the curve 
for univariate data.
Describe a distribution of data, integrating the knowledge of shape 
into the choice for the numerical measures.
Recognize how graphs and choices of numerical summaries can be used 
to misrepresent data without lying.
Create a computer generated report which summarizes the numerical 
characteristics of a data distribution.

2. Predict (estimate) future or current values for a variable based 
on current data, using both manual
calculations and computer software.

Identify linear trends in a bivariate data plot.
Calculate a measure of the strength, direction and form of a linear 
relationship.
Calculate the least squares regression line using calculator and 
computer software.
Assess the fit of the line based on residual plots.
Interpret the coefficient of determination.
Interpret the slope in a "real world" situation.
Evaluate the usefulness of the linear regression as a model for 
predicting the variable being estimated.


3. Incorporate the use of probability and risk into decision making.

Learning Elements Simulate common events which incorporate 
probability in their outcomes.
Discriminate between independent events and mutually exclusive events.
Calculate simple probabilities.
Discriminate between categorical (attribute) data and quantitative 
data.
Identify binomial and normal distributions.
Calculate binomial and normal probabilities.
Identify common real-world occurrences of both the binomial and 
normal distributions.
Describe probability distributions based on simulated common events.

4. Integrate probabilities into estimating population parameters 
based on sample statistics.

Simulate sampling from a normal distribution using computer software.
Differentiate between an experiment which proves causality and a 
survey which shows only a relationship
between variables.
Describe the sampling distribution of the means.
Explain the central limit theorem and its importance in estimating 
population values in such business tools as
statistical process control.
Apply the central limit theorem to solving problems involving a 
sample mean.
Describe the sampling distribution of the proportion.
Recognize the difference between attribute sampling distributions and 
quantitative sampling distributions.
Calculate probabilities using the sampling distribution of the mean 
or the proportion.

5. Use statistical techniques for estimating population means and 
proportions.

Integrate probability concepts and sampling techniques to understand 
the concept of a confidence interval.
Discriminate between sample statistics and population parameters.
Construct a confidence interval for a population mean using both 
manual calculations and computer software.
Construct a confidence interval for a population proportion using 
both manual calculations and computer
software.
Illustrate current examples of confidence intervals found in 
newspapers, websites, journals, etc.
Write a complete statement which explains a calculated confidence 
interval.
Explain the confidence level associated with a confidence interval.
Evaluate a current process based on the confidence interval.

6. Use statistical techniques to determine if a current process or 
belief is true based on sample data.

Formulate a null and alternative hypothesis.
Discriminate between Type I and Type II errors.
Explain what a Type I error measures.
Explain what a Type II error measures.
Investigate the relationship between a Type I and Type II error.
Calculate and interpret the p-value for a test.
Conduct hypothesis tests on population means
Conduct hypothesis tests on population proportions.
Determine the statistical conclusion for the hypothesis test.
Explain with reference to the variable being measured the results of 
the hypothesis test.

7. Use statistical process control techniques to determine if a 
process is running within acceptable
industrial standards.

Differentiate between common cause and special cause variation.
Determine where the responsibility for correcting each type of 
variation should lie within the process
Create X-bar and R charts manually and on computer.
Create X-bar and s charts on the computer.
Determine if a process is in control.
Identify which variation in the control chart is "common cause" and 
which is "special cause" variation.
Differentiate between measurement variables and attribute variables.
Create p-chart for attribute data.
Determine if the process is in control.
Identify common quality standards such is ISO and QS.

8. Use statistical techniques to test categorical and/or multiple 
independent distributions.

Explain the meaning of independence for two distributions.
Determine if two nominal scale variables are independent using 
hypothesis test.
Conduct a full hypothesis test to determine if two or more 
independent samples have equal proportions.
Write a statement in terms of actual variable measured that explains 
the results of the test.

9. Determine if linear regression is a robust model for predicting a 
value.

Determine if the assumptions of least square model are met.
Conduct hypothesis test to determine if the individual parameters in 
linear model are significant.
Interpret the coefficient of determination in terms of the data.
Conduct a hypothesis test to determine if the model as a whole is a 
significant predictor.
Construct confidence interval for the slope of the equation.
Construct a confidence interval for the estimate for any value of the 
dependent variable.
Explain the meaning of the confidence interval for the slope of the 
equation.
Explain the meaning of the confidence interval for the estimate of a 
value for the dependent variable.


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