# QNM223 - Business Statistics Using Excel

 Semester Summer - 2019 Winter - 2019 Fall - 2018 Summer - 2018 Winter - 2018 Fall - 2017 Summer - 2017 Winter - 2017 Fall - 2016 Summer - 2016 Winter - 2016 Fall - 2015 Summer - 2015 Winter - 2015 Fall - 2014 Summer - 2014 Winter - 2014 Schools offering this subject Select school School of International Business & Management School of Marketing School of Marketing and Advertising Last revision date Apr 8, 2019 12:20:06 PM Last review date Apr 8, 2019 12:20:22 PM

Subject Title

Subject Description
The statistical methods of collection, analysis, presentation and interpretation of quantitative data used for making generalizations, projections and decisions under uncertain conditions are introduced.  Emphasis will be on the use of both descriptive and inferential techniques within the workplace.

This course is equivalent to QNM222 in topic content.  We teach the same theory and formulas as QNM222 but include substantial Excel analysis. The course is taught in a hybrid model of 3 hours of classroom instruction plus weekly online digital lectures.  All classes are scheduled in a computer lab so we can use self-paced practice questions that automatically adjust to student ability.

Credit Status
One credit.

Learning Outcomes
Upon successful completion of this subject the student will be able to:

1. Demonstrate the ability to present, describe, and summarize data by:
• identifying the different types of data and levels of measurement.
• differentiating between a population and a sample
• re-organizing raw data into arrays and grouped distributions
• presenting data by means of various tables and graphing methods using computer software/manual techniques
• calculating various measures of location and dispersion, including the mean, median, mode, percentiles, quartiles, standard deviation, and variance
• describing the shape of a distribution
• comparing and evaluating alternative methods of presenting data
2. Evaluate with the use of probability, the likelihood that a statistical inference is correct by:
• defining probability
• differentiating between subjective, relative frequency and classical approaches
• determining when events are mutually exclusive and/or statistically independent
• calculating probabilities using the rules of addition and multiplication
3. Determine the characteristics of selected probability distributions and identify where each distribution can be applied by:
• differentiating between discrete  and continuous random variables
• determining the probability of events and calculating the means (expected value) and standard deviations of:
1. the probability distribution of a discrete random variable
2. the binomial probability distribution
4. Recognize normal distribution problems and know how to solve such problems by:
• calculating the z-value corresponding to any observation on a normal distribution
• determining the probability a random observation is in a given interval on a normal distribution
5. Use the central limit theorem to:
• calculate the mean and standard error of a random variable
• calculate probabilities for a given sample mean
6. Use sample data to make statements about the value of the population mean or proportion by:
• differentiating between a point estimator and an interval estimator
• calculating the point estimate for the population mean or proportion
• determining the confidence intervals for the population mean using sample mean and population standard deviation for large or small samples
• determining the confidence interval for population proportion for large or small samples
• computing the required sample size to estimate the population mean and population proportion for large or small samples
7. Construct and evaluate hypothesis test of a mean or proportion by:
• stating the null hypothesis and the alternative hypothesis
• indicating the appropriate test statistic
• establishing the critical values of the test statistic
• calculating the actual value of the test statistic and drawing appropriate conclusions
8. Perform simple regression and correlation analysis in business situations as indicated by:
• interpreting a scatter diagram
• determining and interpreting the correlation coefficient and the coefficient of determination
• differentiating between a dependent variable and an independent variable
• determining and interpreting the coefficients of the sample regression line
• using a regression equation to predict the value of the dependent variable for a selected value of the independent variable
• conduct a test of hypothesis for the coefficient of correlation and each coefficient of regression
• using spreadsheet software to perform the regression analysis
• interpret confidence intervals and prediction intervals for the dependent variable

Essential Employability Skills
Apply a systematic approach to solve problems.

Use a variety of thinking skills to anticipate and solve problems.

Take responsibility for one's own actions, decisions, and consequences.