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- https://stats.libretexts.org/Bookshelves/Computing_and_Modeling/Book%3A_Linear_Regression_Using_R_-_An_Introduction_to_Data_Modeling_(Lilja)/01%3A_Introduction/1.04%3A_What's_Next%3FChapter 2 describes the sample data that will be used in the examples throughout this tutorial, and how to read this data into the R environment. Chapter 5 then shows how to use this multi-factor regr...Chapter 2 describes the sample data that will be used in the examples throughout this tutorial, and how to read this data into the R environment. Chapter 5 then shows how to use this multi-factor regression model to predict the system response when given new input data. Chapter 6 explains in more detail the routines used to read a file containing your data into the R environment.
- https://stats.libretexts.org/Bookshelves/Computing_and_Modeling/Book%3A_Linear_Regression_Using_R_-_An_Introduction_to_Data_Modeling_(Lilja)/01%3A_Introduction/1.02%3A_What_is_a_Linear_Regression_Model%3FWe also will be able to use the model we develop to predict the performance we would expect to see on a system that has input values that did not exist in any of the systems that we actually measured....We also will be able to use the model we develop to predict the performance we would expect to see on a system that has input values that did not exist in any of the systems that we actually measured. As a final point, note that, since the regression model is a linear combination of the input values, the values of the model parameters will automatically be scaled as we develop the model.
- https://stats.libretexts.org/Bookshelves/Computing_and_Modeling/Book%3A_Linear_Regression_Using_R_-_An_Introduction_to_Data_Modeling_(Lilja)/02%3A_Understand_Your_Data/2.05%3A_Accessing_a_Data_FrameFor example, the following expressions compute the minimum, maximum, mean, and standard deviation of the perf column in the int92.dat data frame: This notation says to use the data in the column named...For example, the following expressions compute the minimum, maximum, mean, and standard deviation of the perf column in the int92.dat data frame: This notation says to use the data in the column named perf from the data frame named int92.dat. This function makes the corresponding data frame local to the current workspace, thereby eliminating the need to use the potentially awkward $ or square-bracket indexing notation.
- https://stats.libretexts.org/Bookshelves/Computing_and_Modeling/Book%3A_Linear_Regression_Using_R_-_An_Introduction_to_Data_Modeling_(Lilja)/04%3A_Multi-factor_Regression/4.05%3A_Residual_AnalysisTo check the validity of the assumptions used to develop our model, we can again apply the residual analysis techniques that we used to examine the one-factor model in Section 3.4. In this plot, we ca...To check the validity of the assumptions used to develop our model, we can again apply the residual analysis techniques that we used to examine the one-factor model in Section 3.4. In this plot, we can see a bit more of a pattern and some obvious nonlinearities, leading us to be slightly more cautious about concluding that the residuals are We should not necessarily reject the model based on this one test, but the results should serve as a reminder that all models are imperfect.
- https://stats.libretexts.org/Bookshelves/Computing_and_Modeling/Book%3A_Linear_Regression_Using_R_-_An_Introduction_to_Data_Modeling_(Lilja)/06%3A_Reading_Data_into_the_R_Environment/6.01%3A_Reading_CSV_filesFor example, when extract_data is called with Int2000 as the ar- gument, the nested paste functions simply concatenate the strings "Spec", "Int2000", and "..average.base.". The final string correspond...For example, when extract_data is called with Int2000 as the ar- gument, the nested paste functions simply concatenate the strings "Spec", "Int2000", and "..average.base.". The final string corresponds to the name of the column in the processors data frame that contains the perfor- mance results for the Int2000 benchmark, "SpecInt2000..average.base.". The argument x is a string with the name of the benchmark program, and y is a string with the name of the desired column.
- https://stats.libretexts.org/Bookshelves/Computing_and_Modeling/Book%3A_Linear_Regression_Using_R_-_An_Introduction_to_Data_Modeling_(Lilja)/04%3A_Multi-factor_Regression/4.03%3A_The_Backward_Elimination_ProcessAs mentioned in the previous section, we must find the right balance in the number of predictors that we use in our model. As a result, the backward elimination process is more likely to include these...As mentioned in the previous section, we must find the right balance in the number of predictors that we use in our model. As a result, the backward elimination process is more likely to include these factors as a group in the final model than is the forward selection process. Although the automated methods can simplify the process, they also make it too easy for you to forget to think about whether or not each step in the modeling process makes sense.
- https://stats.libretexts.org/Bookshelves/Computing_and_Modeling/Book%3A_Linear_Regression_Using_R_-_An_Introduction_to_Data_Modeling_(Lilja)/02%3A_Understand_Your_Data/2.01%3A_Missing_ValuesAny large collection of data is probably incomplete. That is, it is likely that there will be cells without values in your data table. These missing values may be the result of an error, such as the e...Any large collection of data is probably incomplete. That is, it is likely that there will be cells without values in your data table. These missing values may be the result of an error, such as the experimenter simply forgetting to fill in a particular entry. They also could be missing because that particular system configuration did not have that parameter available. Fortunately, R is designed to gracefully handle missing values.
- https://stats.libretexts.org/Bookshelves/Computing_and_Modeling/Book%3A_Linear_Regression_Using_R_-_An_Introduction_to_Data_Modeling_(Lilja)/04%3A_Multi-factor_Regression/4.02%3A_Identifying_Potential_PredictorsAlso called the ‘thermal guideline’ or ‘thermal design point,’ the TDP is provided by the chip manufacturer to the system vendor, who is expected to build a case that accommodates the chip’s thermal r...Also called the ‘thermal guideline’ or ‘thermal design point,’ the TDP is provided by the chip manufacturer to the system vendor, who is expected to build a case that accommodates the chip’s thermal requirements.” From this definition, we conclude that TDP is not really a parameter that will directly affect performance. Finally, we notice that only a few of the entries in the int00.dat data frame include values for the L3 cache, so we decide to exclude the L3 cache size as a potential predictor.
- https://stats.libretexts.org/Bookshelves/Computing_and_Modeling/Book%3A_Linear_Regression_Using_R_-_An_Introduction_to_Data_Modeling_(Lilja)/07%3A_SummaryYour knowledge of the system may also give you good reason to eliminate a parameter as a possible predictor, much as we eliminated TDP as a possible predictor in Section 4.2, or to include some of the...Your knowledge of the system may also give you good reason to eliminate a parameter as a possible predictor, much as we eliminated TDP as a possible predictor in Section 4.2, or to include some of the parameters’ non-linear functions as possible predictors, as we did when we added the square root of the cache size terms to our set of possible predictors.
- https://stats.libretexts.org/Bookshelves/Computing_and_Modeling/Book%3A_Linear_Regression_Using_R_-_An_Introduction_to_Data_Modeling_(Lilja)/08%3A_A_Few_Things_to_Try_NextEvaluate the quality of the one-factor models by discussing the residuals, the p-values of the coefficients, the residual standard errors, the R 2 values, the F-statistic, and by performing appropriat...Evaluate the quality of the one-factor models by discussing the residuals, the p-values of the coefficients, the residual standard errors, the R 2 values, the F-statistic, and by performing appropriate residual analysis. Specifically, fill in the x and y values so that x is the mean of the delta values for the predictions and y is the width of the corresponding 95 percent confidence in- terval.
- https://stats.libretexts.org/Bookshelves/Computing_and_Modeling/Book%3A_Linear_Regression_Using_R_-_An_Introduction_to_Data_Modeling_(Lilja)/01%3A_IntroductionOne of the most fundamental of the broad range of data mining techniques that have been developed is regression modeling. Regression modeling is simply generating a mathematical model from measured da...One of the most fundamental of the broad range of data mining techniques that have been developed is regression modeling. Regression modeling is simply generating a mathematical model from measured data. This model is said to explain an output value given a new set of input values. Linear regression modeling is a specific form of regression modeling that assumes that the output can be explained using a linear combination of the input values.
