Question 11 The oldest and most commonly used cell line in Biology is the Hela cell line, named after Henrietta Lacks, whose cervical cancer cells were taken without her consent in 1951 to create this cell line. The issue of data privacy and consent is very important to data science! You can read more about this topic here. The Hela cell line is missing from our dataset. If we know that the Hela mRNA Expression (Affy) value is 8.2, what is the predicted mRNA Expression (RNAseq) value? Use the values in parameters that we derived in Question 1.7, and assign the result to hela_rnaseq. In [29]: hela_rnaseq = . . . hela_rnaseq In [ ]: grader . check("q1_11") Question 12 Compute the predicted mRNA Expression (RNAseq) values from the mRNA Expression (Affy) values in the pten table. Use the values in the parameters array from Question 1.7, and assign the result to predicted_rnaseq. We'll plot your computed regression line with the scatter plot from after question 1.2 of mRNA Expression (Affy) vs. mRNA Expression (RNAseq). In [32]: predicted_rnaseq = . . . # DON'T CHANGE/DELETE ANY OF THE BELOW CODE IN THIS CELL (pten . with_column( "Predicted mRNA Expression (RNAseq)", predicted_rnaseq) . select ("mRNA Expression (Affy)", "mRNA Expression (RNAseq)", "Predicted mRNA Expression (RNAseq) " ) . scatter ("mRNA Expression (Affy)") ) pit . ylabel("mRNA Expression (RNAseq)" ) ; Fitting a least-squares regression linel Recall that the least-square regression line is the unique straight line that minimizes root mean squared error (RMSE) among all possible fit lines. Using this property, we can find the equation of the regression line by finding the pair of slope and intercept values that minimize root mean squared error