In the conversion of pyruvate to ethanol, what is produced?

During the light-dependent reactions, NADPH ____.

(5 points for the correct answer. If you have correctly answered a total of 9 questions among questions 1 through 11, you will receive 2 bonus points.) Determine the general solution of the following Euler equation:            

Background The dataset includes 9 baseline numeric variables: age, body mass index, average blood pressure, and six blood serum measurements for each of n = 442 diabetes patients. The response of interest is a quantitative measure of diabetes disease progression one year after baseline. The dataset is obtained from sklearn.datasets. We will be fitting multiple linear regression models to the train dataset and making predictions on the test dataset. Attribute Information: age: age in years bmi: body mass index bp: average blood pressure s1: tc, total serum cholesterol s2: ldl, low-density lipoproteins s3: hdl, high-density lipoproteins s4: tch, total cholesterol / HDL s5: ltg, possibly log of serum triglycerides level s6: glu, blood sugar level Target: quantitative measure of disease progression one year after baseline (Response variable) Note: All features have NOT been standardized.

Question 3: Stepwise Regression – 14 points For this question, use the trainData. Perform forward stepwise regression using BIC. Let the minimum model be the one with only an intercept, and the maximum model to be model1. Display the model summary of your final model. Call it forward_model. (2 points) Which variables were selected in the forward_model? Which regression coefficients are significant at the 99% confidence level in forward_model? (1 points) Perform backward stepwise regression using AIC. Let the minimum model be the one with only an intercept, and the maximum model to be model1. Display the model summary of your final model. Call it backward_model. (2 points) Which regression coefficients are significant at the 99% confidence level in backward_model? Are the selected variables different in forward and backward models? (2 points) Perform 2 Partial F-tests to compare the backward_model with the full model (model1) and the forward model with model1. What is your interpretation at the 95% confidence level? (2 points) Perform forward-backward stepwise regression model using AIC, starting with minimal model. Call it both_model.(2 points) Which variables are selected in both_model? Are all the selected variables significant at 99% level? Explain the reason. (2 points) What is the difference in variable selection between forward, backward and forward-backward stepwise regresssion? (1 points)

BONUS (up to 3 points): Explain the similarities and differences between osteomalacia and rickets.

5. Determine the location of the center of pressure (yp), measured from the free water level (top of the water).

Question 1: EDA and Full Model – 4 points For this question, use the trainData. Build a correlation matrix plot on the trainData dataset. Interpret it. (1 points) Fit a multiple linear regression with the variable ‘Target’ as the response and the other variables as predictors. Call it model1. Display the model summary. (2 points) What are the Mallow’s Cp, AIC, and BIC values for the full model (model1)? (1 points)

A spring is attached to the hook of the iolab. The following data is collected showing the spring force (in N) on the vertical axis plotted as a function of position on the horizontal axis (in meters).  From this data, determine the equilibrium position of the spring (approximately). In case you find it hard to read the summary statistics on the right, the slope of the best fit line is approximately -2 and the intercept is approximately 0.4. 

Instructions The R Markdown/Jupyter Notebook file includes the questions, the empty code chunk sections for your code, and the text blocks for your responses. Answer the questions below by completing the R Markdown/Jupyter Notebook file. You may make slight adjustments to get the file to knit/convert but otherwise keep the formatting the same. Once you’ve finished answering the questions, submit your responses in a single knitted file as HTML only. Partial credit may be given if your code is correct but your conclusion is incorrect or vice versa. Next Steps: 1. Save the .Rmd/.ipnyb in your working directory – the same directory where you will download the “diabetes_dataset.csv” data file into. Having both files in the same directory will help in reading the “diabetes_dataset.csv” file.  2. Read the question and create the code necessary within the code chunk section immediately below each question. Knitting this file will generate the output and insert it into the section below the code chunk.  3. Type your answer to the questions in the text block provided immediately after the response prompt.  4. Once you’ve finished answering all questions, knit this file and submit the knitted file as HTML on Canvas.  Mock Example Question  This will be the exam question – each question is already copied from Canvas and inserted into individual text blocks below, you do not need to copy/paste the questions from the online Canvas exam. “`{r}# Example code chunk area. Enter your code below the comment““Mock Response to Example Question:  This is the section where you type your written answers to the question. Depending on the question asked, your typed response may be a number, a list of variables, a few sentences, or a combination of these elements.  Ready? Let’s begin. We wish you the best of luck! Data Set diabetes_dataset.csv Starter TemplatesYou may use either the R Markdown or Jupyter Notebook Starter Template: R Markdown Starter Template: Final_Exam_starter_template_Fall24_R.Rmd Jupyter Notebook Python Starter Template: Final Exam_starter_template_Fall24_Python.ipynb Jupyter Notebook R starter Template: Final_Exam_starter_template_Fall24_R.ipynb