]]>

]]>

]]>

Let the random variable I denote the family nicome, so that

the probability is

Using GDC Casio fx-9860G SD

MAIN MENU > STAT>DIST(F5)>NORM(F1)>Ncd>

Setting Lower: 40,000

Upper: 50,000

: 12,000

: 45,000

We find that the probability is 0.323

So 32.3% of the families will benefit from this new tax law.

Hope these help!!

Statistics: Posted by miranda — Sun Mar 24, 2013 11:32 am

]]>

Let the random variable W denote the weight of the students, so that

We know that

Since we don’t know the mean, we cannot use the inverse normal. Therefore we have to transform the random variable to that of

, using the transformation

we have the following

Using GDC Casio fx-9860G SD

MAIN MENU > STAT>DIST(F5)>NORM(F1)>InvN>

Setting Tail: right

Area: 0.3

:1

:0

We find that the standardized value is 0.5244

Therefore

Hope these help!!

Statistics: Posted by miranda — Sun Mar 24, 2013 11:30 am

]]>

A normal distribution is a continuous probability distribution for a random variable X. The graph of a normal distribution is called the normal curve. A normal distribution has the following properties.

1. The mean, median, and mode are equal.

2. The normal curve is bell shaped and is symmetric about the mean.

3. The total are under the normal curve is equal to one.

4. The normal curve approaches, but never touches, the x-axis as it extends farther and farther away from the mean.

Approximately 68% of the area under the normal curve is between and

and . Approximately 95% of the area under the normal curve is between and . Approximately 99.7% of the area under the normal curve is between and

The standard normal distribution is a normal probability distribution that has a mean of 0 and a standard deviation of 1.

Concerning your question

Let the random variable W denote the weight of the dogs, so that

We know that

Since we don’t know the standard deviation, we cannot use the inverse normal. Therefore we have to transform the random variable to that of

, using the transformation

we have the following

Using GDC Casio fx-9860G SD

MAIN MENU > STAT>DIST(F5)>NORM(F1)>InvN>

Setting Tail: right

Area: 0.3

:1

:0

We find that the standardized value is 0.5244

Therefore

Hope these help!!

Statistics: Posted by miranda — Sun Mar 24, 2013 11:26 am

]]>

How can we find the percentage of the population will benefit from a new tax law

expected to benefit families with income between $40,000 and $50,000 given that the family income follows a normal distribution with mean $45,000 and standard deviation $12,000 ??

Thanks

Statistics: Posted by elizabeth — Sun Mar 24, 2013 11:18 am

]]>

How can we find the mean

of the weight of a population of students which is found to be normally distributed with standard deviation 2 Kg and the 30% of the students weigh at least 53 Kg.

Thanks

Statistics: Posted by elizabeth — Sun Mar 24, 2013 11:17 am

]]>

How can we find the standard deviation of the weight of a population of dogs which is found to be normally distributed with mean 7.4 Kg and the 30% of the dogs weigh at least 8 Kg.

Thanks

Statistics: Posted by elizabeth — Sun Mar 24, 2013 11:16 am

]]>

Here you can find a description of the course not so detailed.

You can find the official syllabus of IB maths SL on the following link of IBO

http://www.ibo.org or http://store.ibo.org

Cumulative distribution functions for both discrete and continuous distributions. Geometric distribution.

Negative binomial distribution. Probability generating functions for discrete random variables.

Using probability generating functions to find mean, variance and the distribution of the sum of n independent random variables.

Linear transformation of a single random variable. Mean of linear combinations of n random variables. Variance of linear combinations of n independent random variables.

Expectation of the product of independent random variables.

Unbiased estimators and estimates. Comparison of unbiased estimators based on variances.

Unbiased estimator for μ, unbiased estimator for .

A linear combination of independent normal random variables is normally distributed. The central limit theorem.

Confidence intervals for the mean of a normal population. Null and alternative hypotheses, and .

Significance level. Critical regions, critical values, p-values, onetailed and two-tailed tests.

Type I and II errors, including calculations of their probabilities. Testing hypotheses for the mean of a normal population.

Introduction to bivariate distributions. Covariance and (population) product moment correlation coefficient ρ.

Definition of the (sample) product moment correlation coefficient R in terms of n paired observations on X and Y. Its application to the estimation of ρ.

Informal interpretation of r, the observed value of R. Scatter diagrams. The following topics are based on the assumption of bivariate normality.

Use of the t-statistic to test the null hypothesis. Knowledge of the facts that the regression of X on Y and Y on X are linear.Least-squares estimates of these regression lines. The use of these regression lines to predict the value of one of the variables given the value of the other.

Statistics: Posted by ib maths — Mon Feb 25, 2013 3:59 pm

]]>

]]>