ourse load, English speaking status, family, and weekly hours spent studying. Let's assume that the Student_Data.xls file was the entire population. We know the mean and standard deviation of student ages to be 42.3 and 8.9, respectively. Using the Normal_ Probability.xls file, compute the percentage of students that are older than 50, younger than 40, between 41 and 46, and oldest 10% are at what age? Then compare to the truth as found in the actual file. ID Gender Major Employ Age MBA_GPA BS GPA Hrs_Studying Works FT 101 0 No Major Unemployed 53 3.01 3.15

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Use the Student_Data which consists of 200 MBA students at Whatsamattu U. It includes variables regarding their age, gender, major, GPA, Bachelors GPA, course load, English speaking status, family, and weekly hours spent studying.

Let's assume that the Student_Data.xls file was the entire population. We know the mean and standard deviation of student ages to be 42.3 and 8.9, respectively. Using the Normal_ Probability.xls file, compute the percentage of students that are older than 50, younger than 40, between 41 and 46, and oldest 10% are at what age? Then compare to the truth as found in the actual file.

ID Gender Major Employ Age MBA_GPA BS GPA Hrs_Studying Works FT
101 0 No Major Unemployed 53 3.01 3.15 6 0
102 0 Leadership Full Time 30 3.3 3.35 6 1
103 0 No Major Part Time 32 3.62 3.6 7 0
104 0 Leadership Full Time 42 3.21 3.4 7 1
105 0 Leadership Full Time 56 3.39 3.4 7 1
106 0 No Major Full Time 46 3.65 3.8 8 1
107 0 Leadership Full Time 49 3.47 3.7 8 1
108 0 No Major Part Time 32 3.44 3.6 7 0
109 0 No Major Full Time 36 3.88 3.95 9 1
110 0 Leadership Full Time 42 3.83 3.95 9 1
111 0 No Major Part Time 37 3.53 3.6 7 0
112 0 No Major Full Time 31 3.22 3.3 6 1
113 0 No Major Full Time 31 3.56 3.8 8 1
114 0 No Major Unemployed 42 3.2 3.25 6 0
115 0 No Major Full Time 39 3.17 3.3 6 1
116 0 No Major Full Time 47 3.41 3.6 7 1
117 0 No Major Part Time 28 3.56 3.7 8 0
118 0 No Major Unemployed 28 3.34 3.6 7 0
119 0 No Major Full Time 52 3.44 3.6 7 1
120 0 No Major Part Time 35 3.76 3.8 8 0
121 0 Finance Full Time 38 3.55 3.45 7 1
122 0 Finance Full Time 44 3.88 3.9 8 1
123 0 Finance Part Time 38 3.31 3.45 7 0
124 0 Finance Full Time 52 3.09 3.15 6 1
125 0 Finance Unemployed 53 3.82 4 9 0
126 0 Finance Part Time 53 3.01 3.2 6 0
127 0 Finance Full Time 31 3.66 3.85 8 1
128 0 Finance Part Time 47 3.64 3.7 8 0
129 0 Finance Full Time 51 3.59 3.65 7 1
130 0 Finance Unemployed 37 3.49 3.55 7 0
131 0 Finance Part Time 46 3.13 3.2 6 0
132 0 Finance Full Time 48 3.83 3.9 8 1
133 0 Finance Full Time 54 3.04 3.15 6 1
134 0 Finance Full Time 48 3.91 4 10 1
135 0 Finance Full Time 36 3.56 3.7 8 1
136 0 Finance Unemployed 39 3.96 4 9 0
137 0 Finance Full Time 28 3.46 3.4 7 1
138 0 Finance Part Time 45 3.22 3.15 6 0
139 0 Finance Full Time 31 3.27 3.2 6 1
140 0 Finance Full Time 47 3.43 3.45 7 1
141 0 Finance Part Time 35 3.85 3.95 9 0
142 0 Finance Full Time 52 3.89 3.9 8 1
143 0 Finance Part Time 52 3.37 3.45 7 0
144 0 Finance Unemployed 55 3.32 3.3 6 0
145 0 Finance Full Time 52 3.54 3.55 7 1
146 0 Finance Part Time 46 3.8 3.9 8 0
147 0 Finance Full Time 31 3.74 3.85 8 1
148 0 Finance Full Time 33 3.17 3.45 7 1
149 0 Finance Part Time 45 3.27 3.55 7 0
150 0 Finance Full Time 50 3.32 3.3 6 1
151 0 Marketing Part Time 33 3.56 3.45 7 0
152 0 Marketing Full Time 37 3.95 4 9 1
153 0 Marketing Unemployed 33 3.56 3.75 8 0
154 0 Marketing Full Time 46 3.79 3.75 8 1
155 0 Marketing Part Time 55 3.93 4 9 0
156 0 Marketing Full Time 30 3.79 3.85 8 1
157 0 Marketing Full Time 51 3.71 3.85 8 1
158 0 Marketing Part Time 35 3.05 3.35 6 0
159 0 Marketing Unemployed 40 3.22 3.2 6 0
160 0 Marketing Part Time 29 3.85 3.95 9 0
161 0 Marketing Full Time 52 3.82 3.95 9 1
162 0 Marketing Full Time 27 3.23 3.95 9 1
163 0 Marketing Full Time 51 3.56 3.65 7 1
164 0 Marketing Part Time 56 3.53 3.65 7 0
165 0 Marketing Full Time 35 3.62 4 9 1
166 0 Leadership Full Time 46 3.8 3.95 9 1
167 0 Leadership Part Time 39 3.47 3.35 6 0
168 0 Leadership Full Time 31 3.64 3.65 7 1
169 0 Leadership Full Time 52 3.03 3.15 5 1
170 0 Leadership Unemployed 32 3.17 3.25 6 0
171 0 Leadership Part Time 32 3.22 3.2 6 0
172 0 Leadership Full Time 44 3.92 4 10 1
173 0 Leadership Full Time 43 3.82 3.95 9 1
174 0 Leadership Part Time 38 3.26 3.55 7 0
175 0 Leadership Full Time 54 3.8 3.85 8 1
176 0 Leadership Full Time 27 3.2 3.2 6 1
177 0 Leadership Part Time 38 3.46 3.35 6 0
178 0 Leadership Full Time 45 3.67 3.75 8 1
179 0 Leadership Unemployed 48 3.06 3.4 7 0
180 0 Leadership Full Time 43 3.66 3.85 8 1
181 0 Leadership Full Time 34 3.96 4 10 1
182 0 Leadership Full Time 54 3.75 3.85 8 1
183 0 Leadership Full Time 36 3.83 3.85 8 1
184 0 Leadership Full Time 45 3.22 3.2 6 1
185 0 Leadership Unemployed 28 3.36 3.35 6 0
186 0 Leadership Full Time 37 3.21 3.25 6 1
187 0 Leadership Full Time 27 3.02 3.15 5 1
188 0 Leadership Full Time 31 3.99 4 10 1
189 0 Leadership Unemployed 45 3.07 3.15 6 0
190 0 Leadership Full Time 48 3.65 3.65 7 1
191 0 Leadership Full Time 50 3.67 3.85 8 1
192 0 Leadership Full Time 32 3.06 3.35 6 1
193 0 Leadership Unemployed 33 3.98 3.7 8 0
194 0 Leadership Full Time 49 3.93 4 10 1
195 0 Leadership Unemployed 27 3.41 3.3 6 0
196 0 Leadership Part Time 28 3.43 3.5 7 0
197 0 Leadership Full Time 36 3.7 3.65 7 1
198 0 Leadership Full Time 35 3.76 3.75 8 1
199 0 Leadership Part Time 47 3.9 3.9 8 0
200 0 Leadership Full Time 33 3.23 3.3 6 1
Variable descriptions
Gender = 0 (male), 1 (female)
Major = student's major
Age = age of student in years
MBA_GPA = overall GPA in the MBA program
BS_GPA = overall GPA in the BS program
Hrs_Studying = average hours studied per week
Works FT = 0 (No), 1 (Yes)
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