The table below shows the number of individuals infected with a
disease t days after its first detected by the
CDC.
| Days | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 |
|---|---|---|---|---|---|---|---|---|
| Infected Individuals | 1032 | 1709 | 2826 | 4148 | 6801 | 11469 | 17541 | 26509 |
For each of the following questions, enter predictions to the
nearest whole individual.
Exponential Regression Model
We used regression to find an exponential equation that best fits
the data above. The equation has form y=abt where:
a = 669.2
b = 1.6
Model r2=0.999
Use the model to predict the number of individuals infected with
the disease after 8 days.
___ individuals
Notice if it looks close to the actual number of individuals after
8 days.
Linear Regression Model
Next we used regression to find a linear equation that best fits
the data above. The equation has form y=b1t+b0 where:
b0 = -6321.4
b1 = 3405.7
Model r2=0.857
Use the model to predict the number of individuals infected with
the disease after 8 days.
___ individuals
Notice if it looks close to the actual number of individuals after
8 days.
Quadratic Regression Model
Finally we used regression to find a quadratic equation that best
fits the data above. The equation has form y=b2t2+b1t+b0
where:
b0=681
b1=−2723.2
b2=3893.5
Model r2=0.994
Use the model to predict the number of individuals infected with
the disease after 8 days.
___ individuals
Notice if it looks close to the actual number of individuals after
8 days.
4) Looking at the graph, the r2 values, the accuracy of the
predictions for 8 days, which model best fits the data?
The table below shows the number of individuals infected with a disease t days after its...
Predicted concentrations of atmospheric carbon dioxide (CO2) in parts per million (ppm) are shown in the table below. (These concentrations assume that current trends continue.) 2000 2050 2100 2150 2200 CO (ppm) 364 467 600 769 987 Year a) Use the graphing calculator to make a scatterplot of the data. Let x represent years after 2000. Does the data follow a linear trend? Explain. b) The graphing calculator allows you to obtain different regression models for the given data (Stat>...
2. Choose a country and research population data in order to fill out the table beloa. Copy the population numbers counted each five years, as shown in the data base, for the years from 1950 to 2000 . Add a column, \(t\), measuring years șince 1945 .b. What is the country you selected? In what part of the world is it? What is the magnitude of its population numbers? \(\left(100,000^{\circ} \mathrm{s}\right.\), millions, hundred millions, billions?) Is it growing or shrinking...
5) The table below shows the number of hours spent per week viewing TV (y), and the number of years of education (x) for 10 randomly selected individuals. 10 15 8 y (hours) x(years) 4 20 16 4 15 12 14 11 16 16 18 12 20 10 12 (15 Points) Find the simple linear regression model a) (10 Points) Find 95% confidence interval for B b) (5 Points) c) Calculate R2
Number of Components Inspection Time 33 85 14 50 7 31 18 59 16 52 12 41 24 72 43 100 6 21 12 42 18 64 8 25 31 79 13 49 12 30 20 62 18 52 20 59 24 73 43 101 17 59 13 45 22 67 13 45 24 69 a-1. Estimate the linear, quadratic, and cubic regression models. Report the Adjusted R2 for each model. (Round answers to 4 decimal places.) a-2. Which model...
B0/2.pl The table below shows the number of state-registered automatic weapons and the murder rate for several Northwestern states. 11.5 8 6.9 3.3 2.3 2.6 2.2 0.4 y 14.1 10.9 9.7 6.8 5.9 6.2 4.7 2 = thousands of automatic weapons y = murders per 100,000 residents 6.2. This data can be modeled by the equation y = 0.852 + 4.11. Use this equation to answer the following: Special Note: I suggest you verify this equation by performing linear regression...
The following data represent the bacterial growth in a liquid culture over a number of days: Day 0 4 8 12 16 20 Amount ×106 67.38 74.67 82.74 91.69 101.60 112.58 Determine the best equation to predict the amount of bacteria after 35 days using the parabolic model. (Round the final answer to two decimal places.) The amount of bacteria after 35 days is .
The table below gives the number of absences and the overall grade in the class for five randomly selected students. Based on this data, consider the equation of the regression line, yˆ=b0+b1xy^=b0+b1x, for using the number of absences to predict a student's overall grade in the class. Keep in mind, the correlation coefficient may or may not be statistically significant for the data given. Remember, in practice, it would not be appropriate to use the regression line to make a...
My last question has gone hours without being answered, so I
am submitting these again. Anyone who could help with these 10
questions would be MASSIVELY appreciated. Thank you!!
11. Find the y-intercept of the equation of the least-squares regression line for the dataset in the table. (1 poins x y 1 15 6 18 7 18 15 24 16 23 22 26 23 27 28 30 33 32 0.52 1.91 15.14 -23.91 12. For the data in the table,...
The table below gives the number of hours ten randomly selected students spent studying and their corresponding midterm exam grades. Using this data, consider the equation of the regression line, yˆ=b0+b1x, for predicting the midterm exam grade that a student will earn based on the number of hours spent studying. Keep in mind, the correlation coefficient may or may not be statistically significant for the data given. Remember, in practice, it would not be appropriate to use the regression line...
Question 3: The Table below summarises the results of traffic monitoring observations at a motorway, in terms of average spacing between vehicles versus vehicle speed. cinema9.58 14.04 19,36 22.68 27 31.32 35.64 39.96 4.28 46 52.9257.2461.5665.88 68.270 Space (m) 11.5 15.3 18.8 19.7 22.5 25.4 27.7 32.2 33.8 42 54.5 60.575.1 77.3 81 100.5 (a) The Figure below shows part of the Minitab output for a linear regression analysis for average spacing against speed. Explain the analysis in the table,...