Learning Objectives for the Second Test
LECTURE 8: The student will be able to:
1) use Minitab to make a matrix plot of the dependent variable against all other independent variables for a multiple regression problem
2) use the matrix plot to identify the best predictor
3) use Minitab to obtain a correlation matrix for all variables in a multiple regression problem
4) use the correlation matrix to determine the best predictor
5) write a hypothesis test to determine whether a variable is significant
6) write a hypothesis test to determine whether a regression model is significant
7) write the equation of the best model for a multiple regression problem using Minitab output
8) calculate a prediction for a multiple regression problem by hand, if appropriate
9) use Minitab to obtain a prediction from a best regression model, if appropriate
10) interpret the regression coefficients
11) identify the statistic that shows how well the model works in explaining the dependent variable
12) interpret the adjusted R-squared
LECTURE 9: The student will be able to:
1) determine whether multicollinearity exists between two variables
2) use the matrix plot to identify possible multicollinearity
3) use the correlation matrix to identify variables that are muliticollinear
4) identify independent variables to include in regression when multicollinearity exists
5) define qualitative variables for qualitative data
6) use Minitab to develop qualitative variables
7) use Minitab to obtain a best multiple regression model that may contain both quantitative and qualitative information
8) calculate a prediction for a multiple regression model containing both quantitative and qualitative variables by hand, if appropriate
9) use Minitab to make a prediction for a multiple regression model containing both quantitative and qualitative variables, if appropriate
LECTURE 10: The student will be able to:
1) match an identifiable problem with assumptions of OLS regression found on a residual plot with a possible solution
2) use Minitab to develop a regression model where all assumptions of OLS regression have been met showing all appropriate statistical output
3) use Minitab to obtain a prediction from a regression model having all assumptions of OLS regression met, if appropriate
4) calculate a prediction by hand for any regression model having all assumptions of OLS regression met, if appropriate
LECTURE 11: The student will be able to:
1) identify any of the four time series components present in a time series graph
2) describe any of the time series components for a time series graph
3) input time series data in time sequence order in the worksheet of Minitab
4) use Minitab to develop a time series graph
5) use Minitab to develop the best regression model for forecasting using trend analysis
6) use Minitab to forecast using any of the trend analysis models
7) write the equation of the best forecast model from Minitab output
8) use the equation of any of the trend analysis models to forecast, by hand
9) Use Minitab to develop seasonal indices
10) interpret the seasonal indices
11) use seasonal indices to deseasonalize/seasonally adjust time series data
12) use seasonal indices to seasonalize/forecast time series data
13) interpret any of the Minitab graphs associated with decomposition
LECTURE 12: The student will be able to:
1) identify the appropriate form of exponential smoothing to use for forecasting with the data given
2) calculate a forecast by hand using simple exponential smoothing
LECTURE 13: The student will be able to:
1) use Minitab to develop forecasts using exponential smoothing
2) use Minitab to develop forecasts that involve the trend equation and the seasonal indices
3) given a trend equation and seasonal indices, calculate forecasts by hand
LECTURE 14: The student will be able to:
1) use Minitab to develop lagged variables
2) use Minitab to develop an autoregressive forecasting model
3) write the equation of the best autoregressive model using Minitab output
4) calculate forecasts by hand using autoregressive models and Minitab output
5) use Minitab to develop forecasts for autoregressive models
6) match appropriate forecasting techniques with time series components found in data given