Question
In: Advanced Math
Using linear regression, model the following data. The table contains world internet users in millions (Source:...
Using linear regression, model the following data. The table contains world internet users in millions (Source: International Telecommunication Union; ICT database). Model the data as a linear function. Note the x and y are tabulated. Your answer needs to be an equation in the form y = mx + b
|
Year, x |
Internet Users, y |
| 1 | 679.8 |
| 2 | 790.1 |
| 3 | 935 |
| 4 | 1047.9 |
| 5 | 1217 |
| 6 | 1402.1 |
| 7 | 1542.5 |
| 28 | 8108.5 |
Solutions
Colby Messinger answered 16 hours agoRelated Solutions
What are the two options to distribute data to millions of users around the world and...
What are the two options to distribute data to millions of users
around the world and which one is
better in terms of services and scalability?
What are the two options to distribute data to millions of users around the world and...
What are the two options to distribute data to millions of users
around the world and which one is better in terms of services and
scalability?
Below is the ANOVA table for a linear regression analysis: Source SS df MS Lin Regression...
Below is the ANOVA table for a linear regression
analysis:
Source
SS
df
MS
Lin Regression
75
1
75
Residual
1500
60
25
Total
1575
61
(a) How many people were tested?
N = ________
(b) What are the tabled (α = .05) and calculated
F-values?
Ftabled = ___________
Fcalc = ___________
(c) What are the two possible values for the product
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r = _______ or _______
(d) Without calculating the interval, will the 95%
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A multiple regression model is to be constructed to model the time spent using the internet...
A multiple regression model is to be constructed to model the
time spent using the internet per week among internet users. The
explanatory variables are age, hours spent working per week and
annual income.
Data has been collected on 30 randomly selected individuals:
Time using internet
(minutes)
Age
Hours working
per week
Annual income
('000)
140
56
39
28
257
35
31
79
163
35
35
34
115
33
52
27
182
45
36
37
214
51
57
80
187...
Run a linear regression using Excel’s Data Analysis regression tool. Construct the linear regression equation and...
Run a linear regression using Excel’s Data Analysis regression
tool. Construct the linear regression equation and determine the
predicted total sales value if the number of promotions is 6. Is
there a significant relationship? Clearly explain your reasoning
using the regression results.
Number of Promotions
Total Sales
3
2554
2
1746
11
2755
14
1935
15
2461
4
2727
5
2231
14
2791
12
2557
4
1897
2
2022
7
2673
11
2947
11
1573
14
2980
Use Excel to estimate a simple linear regression model for the following data (Y is a...
Use Excel to estimate a simple linear regression model for the
following data (Y is a dependent variable and X is an independent
variable):
Y
X
0
-2
0
-1
1
0
1
1
3
2
Fill in Multiple Blanks:
What is the slope of the estimated line? In your
answer, show one (1) digit to the right of the
decimal point, for example, 1.0, 1.2. Apply the appropriate
rounding rule if necessary.
What is the Y-intercept?
a. Using the following R codes to fit the linear regression model for VitC on HeadWt,...
a. Using the following R codes to fit the linear regression
model for VitC on HeadWt, and obtain its summary. Paste the R
output in your homework. cabbages_data <-
read.csv("http://users.stat.umn.edu/~wuxxx725/data/cabbages_data.csv")
cabbages_reg <- lm(VitC ~ HeadWt, data = cabbages_data)
summary(cabbages_reg)
b. State and interpret the value of r 2 from the model summary
output in part a).
c. Calculate the correlation r between HeadWt and VitC, and
state the strength and the direction of the correlation.
d. State the estimated regression...
Using the following R codes to fit the linear regression model for VitC on HeadWt, and...
Using the following R codes to fit the linear regression model
for VitC on HeadWt, and obtain its summary. Paste the R output in
your homework. cabbages_data <-
read.csv("http://users.stat.umn.edu/~wuxxx725/data/cabbages_data.csv")
cabbages_reg <- lm(VitC ~ HeadWt, data = cabbages_data)
summary(cabbages_reg)
a. Using the following R codes to fit the linear regression model for VitC on HeadWt,...
a. Using the following R codes to fit the linear regression
model for VitC on HeadWt, and obtain its summary. Paste the R
output in your homework. cabbages_data <-
read.csv("http://users.stat.umn.edu/~wuxxx725/data/cabbages_data.csv")
cabbages_reg <- lm(VitC ~ HeadWt, data = cabbages_data)
summary(cabbages_reg) b. State and interpret the value of r 2 from
the model summary output in part a). c. Calculate the correlation r
between HeadWt and VitC, and state the strength and the direction
of the correlation. d. State the estimated regression...
The following data contains the characteristics of 30 motorcycles that will be analyzed using a Regression...
The following data contains the characteristics of 30
motorcycles that will be analyzed using a Regression model. You
will study the relationship between a motorcycles' engine power
(measured in horsepower) and the motorcycles' weight (measured in
pounds).
Power (cf): 145 215 165 85 225 87 175 210 158 86 150 150 76 170
208 150 215 225 220 153 175 130 140 165 180 150 190 110 150 86
Weight (lb): 4082 4735 3693 2310 4951 2672 4464 4382 4363...
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