WB

Wary Bees

Numerade Educator
Professor

Biography

I am an experienced instructor who has taught Maths at GCSE and A-level, as well as 11+ and common admission preparation. I can also instruct Physics up to A level, as well as Biology, Chemistry, and Computer Science/Beginners Programming up to GCSE level, having received A*s in all of these courses. In addition to this, I have expertise teaching young children the violin, a task with many transferrable abilities to academic tutoring.

I am adaptable in how I run my tutorials, whether it be assistance with specific difficulties and assignments presented to me by tutees, or a more planned program in which I offer the tutees tasks to work through in the class and sometimes set homework in between meetings. I understand what it takes to get good scores.

Education

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Educator Statistics

Numerade tutor for 4 years
23 Students Helped

Topics Covered

Mastering Motion: Achieving Efficiency Along a Straight Line
Mastering Newton's Laws: Tips for Applying Them Effectively

Wary's Textbook Answer Videos

1

Wary's Quick Ask Videos

0:00
Intro Stats / AP Statistics

For a child living in a particular school district, let voucher_i be a dummy variable equal to one if a child is selected to participate in a school voucher program, and let score_i be that child's score on a subsequent standardized exam. Suppose that the participation variable, voucher_i, is completely randomized in the sense that it is independent of both observed and unobserved factors that can affect the test score.

(a) Suppose you can collect additional background information, such as family income, family structure (e.g., whether the child lives with both parents), and parents' education levels. Do you need to control for these factors to obtain an unbiased estimator of the effects of the voucher program? Explain.

(b) If your main focus is the effect of the voucher program, is there a reason why you might want to include the other background variables, regardless of whether you need to get an unbiased estimate?
02:03
Intro Stats / AP Statistics

. Failure of the upstream slope of an earth dam can be affected
by the rate of that the water in the reservoir behind the dam is
released (a process known as “draw down”). An engineer estimates
that probability of slope failure of the earth dam given the rate
of drawdown (slow, medium, fast) and initial estimates of the
probability of each drawdown rate as follows:
Drawdown Rate
P[ Slope falure | drawdown rate]
P[Drawdown rate]
Slow
.05
.25
Medium
.25
.45
Fast
.75
.3
1
(a) Determine the probability of upstream slope failure during a
future drawdown event.
(b) Given that there is a drawdown-related slope failure, what
is the probability that it will occur as a result of drawdown at a
medium rate?
00:22
Intro Stats / AP Statistics

A small shop has five existing machines (M1 through M5) located
at coordinate location P1 = (8,25), P2 = (10,12), P3 = (16,30), P4
= (30,10) and P5 = (40,25). Two new machines (N1 and N2) are to be
located in the shop. It is anticipated that there will be four
trips per day between the new machines. The number of trips per day
between each machine and existing machine is shown in Table
6.1.
Machine
M1
M2
M3
M4
M5
N1
8
6
5
4
3
N2
2
3
4
6
6
Formulate the objective function assuming rectilinear distance
is used. [5]
Formulate the constraints
[8]
State the variables to be considered. [2]
01:07
Intro Stats / AP Statistics

A real estate company is analyzing the selling prices of residential homes in a given community. 140 homes that have been sold in the past month are randomly selected and their selling prices are recorded. The statistician working on the project has stated that in order to perform various statistical tests, the data must be distributed according to a normal distribution. In order to determine whether the selling prices of homes included in the random sample are normally distributed, the statistician divides the data into 6 classes of equal size and records the number of observations in each class. She then performs a chi-square goodness-of-fit test for normal distribution. The results are summarized in the following table.

Goodness-of-Fit Test
Observed | Expected | O-E | (O-E)^2/E | % of chi-square
10 | 3.192 | 6.808 | 14.520 | 64.81
23 | 19.026 | 3.974 | 0.830 | 3.70
37 | 47.782 | -10.782 | 2.433 | 10.86
40 | 47.782 | -7.782 | 1.267 | 5.66
27 | 19.026 | 7.974 | 3.342 | 14.92
3 | 3.192 | -0.192 | 0.012 | 0.05
140 | 140.000 | 0.000 | 22.404 | 100.00

22.40 | chi-square
.0001 | p-value

At a significance level of .05, we

reject H0; conclude that the residential home selling prices are not distributed according to a normal distribution.
do not reject H0; conclude that the residential home selling prices are not distributed according to a normal distribution.
reject H0; conclude that the residential home selling prices are distributed according to a normal distribution.
do not reject H0; conclude that the residential home selling prices are distributed according to a normal distribution.
01:34
Intro Stats / AP Statistics

The following statistics come from a random sample of 5 measurements of the heat-producing capacity (in millions of calories per ton) of coal from two mines (i.e. they took 5 samples from mine 1 and 5 samples from mine 2 to calculate the following):
Mine 1 : x̄₁ = 8350, s²ₓ₁ = 1620
Mine 2 : x̄₂ = 7800, s²ₓ₂ = 1180
Assume that measurements of the heat-producing capacity follow normal distribution. The variances of measurements of the heat-producing capacity from two mines are the same. Create a 90% confidence interval for the difference between the means of these two samples.
< μₓ₁ − μₓ₂ <
07:11
Intro Stats / AP Statistics

Use the following LCG generator: X_i = (8X_{i-1} + 1)(mod(10)), X_0 = 3 generate 5 PRN's
2.14: Consider the following uniformly distributed random numbers:
U1: .9559, U2: .5814, U3: .6534, U4: .5548, U5: .5330, U6: .5219, U7: .2839, U8: .3734
(a) Generate a uniformly distributed random number with a minimum of 13 and a maximum of 19 using U7.
(b) Generate 1 random variate from an Erlang (r=2, β=3) distribution using U3 and U4.
(c) The demand for magazines on a given day follows the following probability distribution
x: 40, 50, 60, 70, 80
P(X=x): 0.44, 0.22, 0.16, 0.12, 0.06
Instead of using the first four supplied pseudo-random numbers (U1 through U4), start with U4 and generate 4 random variates from the probability distribution for magazine demand.
1 2 3 4