Code:
clc
clear
t = [7 21 35 49 63 77 91];
y = [8.5 21 50 77 89 98 99];
H = 102;
% Fitting linear model
Y = log((H - y)./y);
X = t;
YP = polyfit(X, Y, 1);
b = -YP(1)
a = -YP(2)
xq = 40;
yq = H./(1 + exp(-a - b*40));
disp("Height(in.) at t = 40 is "+num2str(yq));
yy = H./(1 + exp(-a - b.*t));
plot(t, y, 'ro', t, yy, '-b')
legend('data', 'function');
Output:


35 50 77 91 98 49 Day Heightin.85 21 21 63 89 The data can be modeled with a function in the for ...
Complete function long_list_printer.print_list(). When it's
finished, it should be able to print this list,
a = [
[93, 80, 99, 72, 86, 84, 85, 41, 69, 31],
[15, 37, 58, 59, 98, 40, 63, 84, 87, 15],
[48, 50, 43, 68, 69, 43, 46, 83, 11, 50],
[52, 49, 87, 77, 39, 21, 84, 13, 27, 82],
[64, 49, 12, 42, 24, 54, 43, 69, 62, 44],
[54, 90, 67, 43, 72, 17, 22, 83, 28, 68],
[18, 12, 10,...
7. (a) (15 pts.) With Figure 1 below showing shifts A and B, fill in the blank Table 1 showing the computation of the fraction of Bin Hours in each shift for the different time groups. VI V IV Group 1 A 9-12 13-16 17-20 21-24 Sunday Monday Tuesday Wednesday B B Thursday Friday Saturday Figure 1 Table 1 Computation of Fraction of Bin Hours in Each Shift Days Total in Shift A Fraction in Each Shift B Fraction in...
Use the moving average method to forecast period 105.Use the exponential smoothing method to forecast period 105.Use the time-series decomposition method to forecast period 105.Comparing the three methods, which one fits this situation best?The larger the parameter (n) is set, the more historical data are taken into account by the moving average.You can choose different parameter (n) to extrapolate to compare the prediction effect.In general, the parameter (n) should not be taken too large.Moving average.153025703560453055106560761085609580106101165012700136701470015760167301776018820197802090021840227702382024800257602676027770287902976030740317203267033690344703567036690376203865039610406204164042590436104460045630466004763048640496105059051610526305366054640558105679057820586505971060700616706269063730647306576066790678106887069890708707189072880739307498075900768607789078880798708084081860829108387084860858408654087780887508978090760917109273093750947509571096750977209877099740100750101760102780103800104850105Exponential smoothingsame153025703560453055106560761085609580106101165012700136701470015760167301776018820197802090021840227702382024800257602676027770287902976030740317203267033690344703567036690376203865039610406204164042590436104460045630466004763048640496105059051610526305366054640558105679057820586505971060700616706269063730647306576066790678106887069890708707189072880739307498075900768607789078880798708084081860829108387084860858408654087780887508978090760917109273093750947509571096750977209877099740100750101760102780103800104850105Time-series decomposition153025703560453055106560761085609580106101165012700136701470015760167301776018820197802090021840227702382024800257602676027770287902976030740317203267033690344703567036690376203865039610406204164042590436104460045630466004763048640496105059051610526305366054640558105679057820586505971060700616706269063730647306576066790678106887069890708707189072880739307498075900768607789078880798708084081860829108387084860858408654087780887508978090760917109273093750947509571096750977209877099740100750101760102780103800104850105
Student stress at final exam time comes partly from the
uncertainty of grades and the consequences of those grades. Can
knowledge of a midterm grade be used to predict a final exam grade?
A random sample of 200 BCOM students from recent years was taken
and their percentage grades on assignments, midterm exam, and final
exam were recorded. Let’s examine the ability of midterm and
assignment grades to predict final exam grades.
The data are shown here:
Assignment
Midterm
FinalExam...
6. Suppose r0-0x8000, and the memory layout is as follows: Address Data 0x8007 0x8006 0x8005 0x8004 0x8003 0x8002 0x8001 0x8000 0x79 0xCD 0xA3 0xFD 0xOD 0xEB 0x2C 0x1A Suppose the system is set as little endian. What are the values of rl and r0 if the instructions are executed separately? (a) LDR (b) LDR (c) LDR (d) LDR rl, r1, r1, r1, [re] [r0, #41 [r0], #4 [r0, #41 ! r1 - r1 - r0 - Appendix 1: ASCII Table...
please peovide coding in R:
with a data of 13 variables and 200 observations
Using the variable CLASS, test at 5% significance level to
test the claim that the
proportions of Freshmen, Sophomores, Juniors, and Seniors are
the same.
2. Repeat part (a) for the variable COLLEGE.
RLM ENGLISH MATH COMP 21 16 25 839SSESSOR 8 F 17 F N H ABCD 1 SEX HSP GPA AGE CREDITS CLASS COLLEGE MAJOR RESIDENCY TYPE 2 Transfer Blochm Resident F 75 2.39...
The data from data95.dat contains information on 78 seventh-grade students. We want to know how well each of IQ score and self-concept score predicts GPA using least-squares regression. We also want to know which of these explanatory variables predicts GPA better. Give numerical measures that answer these questions. (Round your answers to three decimal places.) (Regressor: IQ) R 2 : (Regressor: Self-Concept) R 2 : Which variable is the better predictor? IQSelf Concept obs gpa iq gender concept 1 7.94...
U (%) T, U (%) т, U (%) T, 34 0.0907 0.377 68 0.00008 35 0.0962 69 0.390 1 0,0003 0.00071 0,00126 2 36 0.102 70 0.403 3 37 0.107 71 0.417 2H 0.113 4 38 72 0.431 5 39 73 0,00196 0.119 0.446 6 0.00283 0.00385 0.126 74 0.461 40 7 41 0.132 75 0.477 42 0,00502 0,00636 0.138 0.145 76 0.493 9 43 77 0.511 0.00785 10 44 45 0.152 78 0.529 0.547 11 0.0095 0.159 79 12...
You have two groups of apples (data below). You want to see if there is a statistical differnce between the two groups. Run descriptives and a two tailed, two sample assuming equal variance t-test. Here's your data: Weight of Apples in Grams Apple ID Farm A Farm B 1 131 151 2 147 159 3 134 162 4 134 158 5 136 159 6 137 160 7 140 150 8 134 160 9 136 160 10 133 160 11 134...
The subjects in the data are college students. In the data, id is student ID, anxiety is student’s anxiety score via Anxiety Scale, selfest is student’s self-esteem score via Rosenberg Self-esteem Scale, GPA is student’s GPA; for gender, 0=female, 1=male; for grade, 1=freshman, 2=junior, 3=senior. We have known that population mean for Anxiety Scale is μ=60 with σ=10. Raise relevant questions ( 2 questions is fine) about the data extensively, the questions can be either about descriptive analysis or inferential...