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MAT 1540 Assessment (2023/2024)
The following outcomes will be graded on a four-point scale (0=Incorrect; 1=Partially Correct; 2=Generally Correct; 3=Exemplary). An overall grade will be assigned based on the results of each of the individual outcomes.
SUNY Gen Ed Outcomes
Outcome
Score
SUNY Standard #1 (Interpretation)
SUNY Standard #2 (Representation)
SUNY Standard #3 (Methods)
A total SUNY Gen-Ed score of:
0–3: student does not meet our expectation for the mathematics-learning outcome
4–5: approaching our expectation
6–8: meets our expectation
8–9: exceeds our expectation
The SUNY Standards:
Standard 1: Students will demonstrate the ability to interpret and draw inferences from mathematical models such as formulas, graphs, tables, and schematics.
Standard 2:
Students will demonstrate the ability to represent mathematical information symbolically, visually, numerically and verbally.
Standard 3:
Students will demonstrate the ability to use arithmetical, algebraic, geometric and statistical methods to solve
problems.
You must provide a statistically valid justification (including supporting Rguroo graphs/output)
for your answers.
You may provide hand computations wherever you feel more comfortable with them, but you are strongly encouraged to
use Rguroo or a graphing calculator wherever possible. Updated 9/2023
Below is climate-related data for 18 G-20 countries. Each measurement is the change in value from 2000 to 2020
. In general, positive values indicate that the measures have increased from 2000 to 2020, 0 indicates the measure has not changed from 2000 to 2020, and negative values indicated that the measures have decreased from 2000 to 2020. See the definition of each variable below the table.
Country
Land
Cover
Climate-
Related
Disasters
Carbon
Stock in
Forests
Surface
Temperature
Change
CO
2
Emissions
Renewable
Energy
Consumption
Total
Annual
Precipitatio
n
Argentina
3.47577
-6
-372.387
1.309
-0.2
-0.01
-224.78
Australia
2.26854
-2
-29.188
1.268
-3
2.47
-163.91
Brazil
10.36652
1
-3882.68
0.982
0.1
7.39
-226.15
Canada
1.01752
1
-946.63
-0.163
-3.2
1.81
-5.54
China
-2.40463
-13
2470.03
6
1.156
5.1
-14.82
45.86
France
0.06003
0
377.87
1.376
-2.1
7.55
-145.01
Germany
-0.45589
1
183.51
0.752
-2.8
14.9
-96.14
India
-3.23532
-2
155.818
0.17
0.7
-11.06
189.96
Indonesia
-2.0105
18
-917.547
1.047
0.8
-23.62
64.73
Italy
0.4127
3
163.766
5
0.659
-3
13.57
-32.78
Japan
1.70386
0
402.205
0.801
-1.3
4.75
244.06
Mexico
0.71049
1
143.405
1.092
-0.9
0.16
-33.81
Russia
1.01536
-19
3951.77
2.372
0.5
0.22
9.57
Saudi Arabia
11.04456
1
0
0.701
2.7
0.05
7.34
South Africa
5.83502
-3
-32.808
0.619
0.6
-6.42
-170.38
South Korea
145.0823
0
295.223
7
1.137
1.5
2.93
374.14
United Kingdom
-0.52863
-3
71.816
0.649
-4.4
12.54
-64.21
United States
2.15767
-7
2203.2
0.324
-7.5
5.73
55.06
Land Cover
: An index value that measures changes in land cover over time, grouping land cover into those types that have climate influencing (negative), climate regulating (positive) and climate neutral (0) impacts. A value such as 3.47577 indicates that the land cover has become more climate regulating from 2000 to 2020.
Climate-Related Disasters
: Trend in number of climate related natural disasters. A value such as -6 indicates that the number of climate related disasters per year decreased by 6 from 2000 to 2020.
Carbon Stock in Forests
: Forest carbon stock is the amount of carbon that has been sequestered from the atmosphere and is now stored within the forest ecosystem, mainly within living biomass and soil, and to a lesser
extent also in dead wood and litter. A value such as -372.387 means that the amount of carbon stored in the forest and not in the air has decreased from 2000 to 2020.
Surface Temperature Change
: Annual estimates of mean surface temperature change are measured with respect to a baseline climatology. A value such as 1.309 indicates an increase in temperature from 2000 to 2020.
CO
2
Emissions
: Carbon dioxide emissions of a country, measured in metric tons per capita. A value such as -0.2 means that there are 0.2 fewer metric tons per capita in 2020 than in 2000.
Renewable Energy Consumption
: The percentage of total energy consumption that comes from renewable energy. A value such as -0.01 means that the percentage of renewable energy has decreased 0.01% from 2000 to
2020.
Total Annual Precipitation
: water released from clouds in the form of rain, freezing rain, sleet, snow, or hail, measured in millimeters. A value such as -224.78 means that there was 224.78mm less precipitation in 2020 than 2000.
Updated 9/2023
Sources: World Bank Group, Climate Change Knowledge Portal
International Monetary Fund. 2022. Climate Change Indicators Dashboard.
https://climatedata.imf.org/pages/access-data
. Accessed on 2023-09-11.
Some of these variables make better predictor variables and some make better response variables. Spend some time thinking about what you suspect could be pairs of response and explanatory (predictor) variables. Justify your answers below with the appropriate statistical information when needed.
1)
For which pair of variables is a least squares regression model inappropriate
? Since there is no discernible linear relationship then the least squares regression model is inappropriate. So land cover and total annual precipitation would be inappropriate for a least squares regression model. 2)
Now focus on a pair of variables that is best
fit for a least squares regression model/equation.
a)
How did you decide on this pair of variables? Lets use surface temp change and total annual precipitation, the decision is based on the hypothesis that the amount of total annual precipitation may affect the surface temp change b)
Which variable should be the explanatory variable, and which should be the response variable? Explain
your choices. Surface temp change is the response and total annual precipitation is the explanatory variable. This decision was made with the expectation that total annual precipitation would affect surface temp change.
c)
What is the least squares regression model and what statistical support do you have that this model has any value? y= -.00052x+.8979 Least squares regression is used to predict the behavior of dependent variables it provides the overall rational for the placement of the line of best fit among the data points being studied. 3) Consider your least squares regression model/equation. a)
Interpret the slope of your model.
the slope is interpreted as the change of y for a 1 unit change in x, so for every unit change in annual precipitation the surface temp decreases by .00052
b)
Interpret the intercept of your model, and if the interpretation is inappropriate, explain why. The y-
intercept is the estimated value of y when x=0. So without any annual precipitation the surface temp would change by .8979
c) Give a specific example of a prediction that could be made with your model and interpret your answer.
A decrease in total annual precipitation would increase the surface temp change. For example a total annual precipitation value of -224.78 means there was 224.78mm less precipitation which would lead to a increased surface temp change of 1.014. Updated 9/2023
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