# 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.