QNM222 - Business Statistics
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|Last revision date||2017-01-30 00:04:04.131|
|Last review date||2017-03-13 00:15:04.602|
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 statistical techniques within the workplace. Students may utilize spreadsheet software to present and analyse data.
QNM 222 is a one credit subject.
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
- 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 Spreadsheet software may be used to assist in these calculations
- 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:
- the probability distribution of a discrete random variable
- 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
- using the normal distribution to approximate the binomial 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 sized samples
- determining the confidence interval for population proportion for large or small sized samples
- computing the required sample size to estimate the population mean and population proportion for large or small sized 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 value(s) 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 coefficent of correlation and each coefficent of regression
- using spreadsheet software to perform the regression analysis
- interpret confidence intervals and prediction intervals for the dependent variable
Essential Employability Skills
Execute mathematical operations accurately.
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.
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