Furthermore I often had to print more copies of the same drawing and, printing from the CAD program, I obtained my copies disposed in a single column on the plotter output, while they could have been placed, for example, more than just one in a row. I always had a remnant of the aside unused paper, and even if I used cheap material, nevertheless it was a waste. The first idea of FitPlot comes out of a demand, in my daily work, to print wide format documents (typically A0, A1, A2 produced by a CAD program) on a 42" plotter. print the so arranged plotter sheet and eventually save it for later printings.arrange all objects in a WYSIWYG environment moving, rotating and scaling them.place various pdfs (or other common graphic formats files such as jpg, tif, gif, eps, psd, image in the pasteBoard) on a virtual plotter sheet.We simply plug in Mexico sporty to our line. R squared is 0.7399 That means 73.99% of our variation can be explained by the data and roughly 26% cannot be. For party we want to calculate R squared and we want to interpret what it means.
Do you want to plot why hat? Making sure the plot are X and Y means we do so in the scatter plot on the left.
#FITPLOT R9 PLUS#
Why hat equals negative 0.748 plus 0.161 X. Because equals negative 7.748 which gives us the line of best fit. N and R sums B is 0.161 And then plugging in B and R means into A. Then we can find the line of best fit by using the form with or be given on the right, which again takes in R. X and Y are given by the following formulas. And we want to find the equation of the line of best fit. The correlation coefficient R is given by the following formula where we input our sample size and and our sums to obtain are equal 60.60 Next part C. Remember that these sums can simply found by following the formulas that are stated as in some of the X values some Y and so on.
We want to compute the sums which we've already listed and the correlation coefficient R. Start off with we want to produce a scatter plot for this data which we've already included on the left. Listen to the top of this white board, we want to answer the following six questions in order A through F. (f) For a neighborhood with $x=40$ jobs, how many are predicted to be entry-level jobs? (a) Draw a scatter diagram displaying the data. (Recall that k and kf add upto 1, by implication, kf can range from 0.50 to 0.25, depending on the value of ks.) Perform sensitivity analysis on the expected overall score for the two jobs by varying ks over this range: Is the forest job preferred for all values of ks between 0.50 and 0.75: Sam does believe though that k could range from 0.50 up to 0.75. Suppose Sam Chu is uncomfortable with the precise assessment that ks = 0.60. A sensitivity analysis of the trade-off weight though, can reveal whether a decision maker must make more precise judgment Reconsider the summer-job example described and ana- Iyzed in Chapter In the analysis_ we used trade-off weights of 0.60 for salary and kf = 0.40 for fun (see Figure 4.281. Many decision makers experience difficulty in assessing trade-off weights. An important application of sensitivity analysis occurs in problems involving multiple attributes. What did you understand from the chart'Ĩ months, 2 weeks ago '5.9. (6) Develop the tornado diagram for strategy Forest job and explain What did You understand from the diagram: (a) Develop Senstivity Graph for Ks and Kc: What did yOu understand from the graph A sensitivity analysis of the trade-off weight though, can reveal whether a decision maker must make more precise judgment Reconsider the summer-job example described and ana- Iyzed in Chapter In the analysis we used trade-off weights of 0.60 for salary and kf = 0.40 for fun (see Figure 4.281.
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