The marks for a college math exam are normally distributed wha standard deviation of 9. Marks over 100 are possible.Determine the percentage of students who fail the exam. Assume a pas is 50 or greater. Determine the percentage of students who receive a mark between 80 and 100. Thearemarksaremeandeviation.whoandnormallyof 74offossible!@ Determinewhopassing6 Determineforthefail thegradethereceive100.andqadistributeaCollegeexam.isaMarksDATE:PercentageStandardoverexamwith a100of StudentsAssumea50orgreates.Percentage of studentsmarkbetween 80.tthsuewe

The marks for a college math exam are normally distributed with mean 74 and standard deviation of 9.…

Answered: Determine the percentage of students…

MATLAB: An Introduction with Applications

6th Edition

ISBN:9781119256830

Author:Amos Gilat

Publisher:Amos Gilat

Chapter1: Starting With Matlab

Section: Chapter Questions

Problem 1P

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The marks for a college math exam are normally distributed wha standard deviation of 9. Marks over 100 are possible.

Determine the percentage of students who fail the exam. Assume a pas is 50 or greater.
Determine the percentage of students who receive a mark between 80 and 100.

The
are
marks
are
mean
deviation.
who
and
normally
of 74
of
fossible!
@ Determine
who
passing
6 Determine
for
the
fail the
grade
the
receive
100.
and
q
a
distribute
a
College
exam.
is
a
Marks
DATE:
Percentage
Standard
over
exam
with a
100
of Students
Assume
a
50
or
greates.
Percentage of students
mark
between 80.
t
th
sue
we

Features Features Normal distribution is characterized by two parameters, mean (µ) and standard deviation (σ). When graphed, the mean represents the center of the bell curve and the graph is perfectly symmetric about the center. The mean, median, and mode are all equal for a normal distribution. The standard deviation measures the data's spread from the center. The higher the standard deviation, the more the data is spread out and the flatter the bell curve looks. Variance is another commonly used measure of the spread of the distribution and is equal to the square of the standard deviation.

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