| C1-T |
INTERNET |
GDP |
CO2 |
CELLULAR |
FERTILITY |
LITERACY |
| Algeria |
0.65 |
6.09 |
3 |
0.3 |
2.8 |
58.3 |
| Argentina |
10.08 |
11.32 |
3.8 |
19.3 |
2.4 |
96.9 |
| Australia |
37.14 |
25.37 |
18.2 |
57.4 |
1.7 |
100 |
| Austria |
38.7 |
26.73 |
7.6 |
81.7 |
1.3 |
100 |
| Belgium |
31.04 |
25.52 |
10.2 |
74.7 |
1.7 |
100 |
| Brazil |
4.66 |
7.36 |
1.8 |
16.7 |
2.2 |
87.2 |
| Canada |
46.66 |
27.13 |
14.4 |
36.2 |
1.5 |
100 |
| Chile |
20.14 |
9.19 |
4.2 |
34.2 |
2.4 |
95.7 |
| China |
2.57 |
4.02 |
2.3 |
11 |
1.8 |
78.7 |
| Denmark |
42.95 |
29 |
9.3 |
74 |
1.8 |
100 |
| Egypt |
0.93 |
3.52 |
2 |
4.3 |
3.3 |
44.8 |
| Finland |
43.03 |
24.43 |
11.3 |
80.4 |
1.7 |
100 |
| France |
26.38 |
23.99 |
6.1 |
60.5 |
1.9 |
100 |
| Germany |
37.36 |
25.35 |
9.7 |
68.2 |
1.4 |
100 |
| Greece |
13.21 |
17.44 |
8.2 |
75.1 |
1.3 |
96.1 |
| India |
0.68 |
2.84 |
1.1 |
0.6 |
3 |
46.4 |
| Iran |
1.56 |
6 |
4.8 |
3.2 |
2.3 |
70.2 |
| Ireland |
23.31 |
32.41 |
10.8 |
77.4 |
1.9 |
100 |
| Israel |
27.66 |
19.79 |
10 |
90.7 |
2.7 |
93.1 |
| Japan |
38.42 |
25.13 |
9.1 |
58.8 |
1.3 |
100 |
| Malaysia |
27.31 |
8.75 |
5.4 |
31.4 |
2.9 |
84 |
| Mexico |
3.62 |
8.43 |
3.9 |
21.7 |
2.5 |
89.5 |
| Netherlands |
49.05 |
27.19 |
8.5 |
76.7 |
1.7 |
100 |
| New Zealand |
46.12 |
19.16 |
8.1 |
59.9 |
2 |
100 |
| Nigeria |
0.1 |
0.85 |
0.3 |
0.3 |
5.4 |
57.7 |
| Norway |
46.38 |
29.62 |
8.7 |
81.5 |
1.8 |
100 |
| Pakistan |
0.34 |
1.89 |
0.7 |
0.6 |
5.1 |
28.8 |
| Philippines |
2.56 |
3.84 |
1 |
15 |
3.2 |
95 |
| Russia |
2.93 |
7.1 |
9.8 |
5.3 |
1.1 |
99.4 |
| Saudi Arabia |
1.34 |
13.33 |
11.7 |
11.3 |
4.5 |
68.2 |
| South Africa |
6.49 |
11.29 |
7.9 |
24.2 |
2.6 |
85 |
| Spain |
18.27 |
20.15 |
6.8 |
73.4 |
1.2 |
96.9 |
| Sweden |
51.63 |
24.18 |
5.3 |
79 |
1.6 |
100 |
| Switzerland |
30.7 |
28.1 |
5.7 |
72.8 |
1.4 |
100 |
| Turkey |
6.04 |
5.89 |
3.1 |
29.5 |
2.4 |
77.2 |
| United Kingdom |
32.96 |
24.16 |
9.2 |
77 |
1.6 |
100 |
| United States |
50.15 |
34.32 |
19.7 |
45.1 |
2.1 |
100 |
| Vietnam |
1.24 |
2.07 |
0.6 |
1.5 |
2.3 |
90.9 |
| Yemen |
0.09 |
0.79 |
1.1 |
0.8 |
7 |
26.9 |
Question 1:
You are provided with data for several nations from the Human Development Report, 2003.
a. Compute summary statistics (mean, median, mode, standard deviation) for the variables of GDP (GDP per capita in thousands of dollars) and Internet use and interpret them.
b. Generate a scatterplot with clearly labeled axes for Internet use and GDP.
c. Compute the correlation between Internet use and GDP and interpret it. Refer to both the strength and direction of the correlation in your interpretation. Also interpret the correlation as a coefficient of determination (r-squared).
Question 2:
You are given data about the per capita personal income and infant mortality rate for a number of Tennessee Counties during 2010.
a. Generate a scatterplot of the two variables. What might this relationship imply?
b. Compute the Pearson's correlation coefficient between the two variables.
c. Provide an interpretation of the correlation obtained. Refer to both the strength and direction of the correlation in your interpretation.
d. Also interpret the correlation in terms of r-squared (coefficient of determination). Be careful not to infer cause and effect.
Question 3:
Using the data provided from the Human Development Report of 2003, conduct a regression to predict internet use from GDP. Show the results of the regression.
a. What is the value of the intercept a?
b. What is the value of the slope b?
c. What is the predicted Internet use for a country with a GDP of 32.4 thousand dollars?
d. Interpret the value of the R-squared for the regression model.
Question 4:
Using data provided from theUS Statewide Crime data set, construct a scatterplot using Excel or any software (SPSS or Minitab) between the variables "murder rate" and "poverty". Provide an appropriate title for the chart as well as labels for both axes.
a. There seems to be a problem caused by the presence of an outlier. Identify the outlier and delete it (simply erase its value, do not replace with zero).
- Show the new scatterplot.
- Describethe pattern that emerges. What might this relationship imply? Be careful not to infer cause and effect.
b. Compute the correlation coefficient between the two variables and interpret this correlation. Refer to both the strength and direction of the correlation in your interpretation. Also interpret the correlation in terms of r-squared (coefficient of determination).
c. Conduct a regression to predict murder from poverty (when the observation for DC is removed from the data set) and interpret the results.
d. What is the predicted value for a state with a poverty rate of 13.4?
e. Interpret the value of the R-squared for the regression model.