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MLB Player Salaries & Dark Chocolate's Vascular Health Impact

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Understanding the distribution of sample means is crucial in statistics. Let's analyze two distinct scenarios.

MLB Player Salaries in 2012

In 2012, there were 855 major league baseball players. The mean salary was \(\mu = 3.44\) million dollars, with a standard deviation of \(\sigma = 4.70\) million dollars. We will examine random samples of size \(n = 50\) players to understand the distribution of their mean salaries.

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Distribution of Sample Means

To describe the shape, center, and spread of the distribution of sample means, we apply the Central Limit Theorem (CLT). The CLT states that for sufficiently large sample sizes (typically \(n \ge 30\)), the sampling distribution of the sample mean will be approximately normal, irrespective of the population... Continue reading "MLB Player Salaries & Dark Chocolate's Vascular Health Impact" »

Business and Financial Math: Cost, Revenue & Interest Formulas

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Cost, Revenue, and Break-Even

Cost = VariableCost + FixedCost     Revenue = X * price     Break-even condition: P(x) = 0

C(x) = 8x + 100             R(x) = 10x             R(x) = C(x)         Profit = Revenue - Cost

Profit Function and Example

P(x) = R(x) - C(x) = 10x - (8x + 100) = 2x - 100

Demand and Supply Equilibrium

Demand: demand as a function of unit price P: Qd = a - bP. Equilibrium when D = S.

Supply: q (# items) as a function of unit price P. Example (demand): q = -20p + 800.

Example supply: q = 10p - 100 (supply). Solve equilibrium: -20p + 800 = 10p - 100 → -30p = -900 → p = $30 (equilibrium price). Then q = -20(30) + 800 → q = 200 (equilibrium quantity).


Compound Interest and Future Value

Variables: P = present value,

... Continue reading "Business and Financial Math: Cost, Revenue & Interest Formulas" »

Matrix Determinant and Adjoint Verification with AP/GP and CI

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Matrices (Question 6a)

Verify that A · (\text{adj } A) = (\text{adj } A) · A = |A| · I_3 for
A = \begin{bmatrix} 2 & 3 & 4 \\ 3 & 0 & 1 \\ 2 & 1 & 5 \end{bmatrix}.

Tasks:

  • Find the determinant |A|:
  • Find the Adjoint (\text{adj } A): This involves finding the cofactor of each element and then transposing the resulting matrix.
  • Cofactors: C11 = -1, C12 = -13, C13 = 3, C21 = -11, C22 = 2, C23 = 4, C31 = 3, C32 = 10, C33 = -9
  • Multiply A · (\text{adj } A)

4. Financial Arithmetic (Question 2g)

Find the compound interest on Rs. 8,000 for 1 1/2 years at 10% per annum, compounded annually.

  • Amount for the first year:
  • Interest for the next half year: Use simple interest on the new principal.
  • Total Compound Interest:

Answers use standard... Continue reading "Matrix Determinant and Adjoint Verification with AP/GP and CI" »

Calculating Annuity Due and Sinking Fund Surplus

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Calculating the Future Value of an Annuity Due

Step 1: Determine the Variables

The problem provides the following details:

  • Annual payment: Rs. 200. Therefore, the half-yearly payment (Pmt) is:
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    Rs. 200 / 2 = Rs. 100
    Rs. 200 / 2 = Rs. 100
  • Annual interest rate (r): 4% or 0.04. Since the interest is compounded half-yearly, the interest rate per period (i) is:
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    0.04 / 2 = 0.02
    0.04 / 2 = 0.02
  • Term: 20 years. Payments are made half-yearly, so the total number of periods (n) is:
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    20 × 2 = 40
    20 × 2 = 40
  • The annuity type is an annuity due, meaning payments are made at the beginning of each period.

Step 2: Apply the Future Value Formula

The formula for the Future Value (FV) of an annuity due is given by:

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FV = Pmt × [((1 + i)^n - 1) / i] × (1 + i)
FV = Pmt × [((1
... Continue reading "Calculating Annuity Due and Sinking Fund Surplus" »

Statistical Process Control Charts and Business Value Metrics

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Customer Value: (Quality, Time, Flexibility, Customer Experience, Innovation) / Price

Sustainability Paradigms: Economic (Viable, Equitable), Environment (Viable, Bearable), Social (Bearable, Equitable)

- Efficiency (Optimization), Differentiator (Innovation), Driver (Motivation) | - Audits (Assess Sustainability Performance)


Table 7.3: Factors for Calculating Three-Sigma Limits for the X (Bar) Chart and R-Chart

Size of Sample (n)Factor for UCL and LCL for X (Bar) Charts (A2​)Factor for LCL for R-Charts (D3​)Factor for UCL for R-Charts (D4​)
21.88003.267
31.02302.575
40.72902.282
50.57702.115
60.48302.004
70.4190.0761.924
80.3730.1361.864
90.3370.1841.816
100.3080.2231.777

*A sample is out of control if its value falls below the LCL or above the UCL*

... Continue reading "Statistical Process Control Charts and Business Value Metrics" »

5 hr 20 min 20 sec corresponds to a longitude difference of

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lim x->0 sinx/x = 1 | H.A.: compare degrees | V.A.: denom = 0 | Continuous if: f(a), lim, equal

DERIVATIVES: (x^n)'=nx^(n-1), (e^x)'=e^x, (a^x)'=a^x ln a, (lnx)'=1/x | (uv)'=u'v+uv', (u/v)'=(u'v-uv')/v^2 |chain: (f(g(x)))'=f'(g(x))g'(x)

TRIG DERIVATIVES: (sin)'=cos, (cos)'=-sin, (tan)'=sec^2 | (sec)'=sec·tan, (csc)'=-csc·cot, (cot)'=-csc^2

CRITICAL POINTS:f'=0 or DNE ⇒ crit pt | f'>0 inc | f'<0 dec | f''>0 conc up | f''<0 conc down | inflec = f'' signchange

INTEGRATION: ∫x^n dx = x^(n+1)/(n+1)+C | ∫e^x dx = e^x+C | ∫a^x dx = a^x/ln a+C | ∫1/x dx = ln|x|+C | ∫sin x dx

= -cos x+C | ∫cos x dx = sin x+C

FTC: Part 1: d/dx ∫_a^x f(t) dt = f(x) | Part 2: ∫_a^b f(x) dx = F(b)-F(a)

AREA & VOLUME: A = ∫_a^b (top - bot)... Continue reading "5 hr 20 min 20 sec corresponds to a longitude difference of" »

R Programming Fundamentals, SQL, and Advanced Clustering Methods

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Section A: R Basics and Data Types (Weeks 1-4)

Model Questions and Answers

Q: Create a vector v (3, NA, Inf, -Inf). Explain adding 5 to v.

A: The operation is element-wise. Missing values (NA) propagate, resulting in NA. Infinite values (Inf, -Inf) remain infinite.

v <- c(3, NA, Inf, -Inf)
print(v + 5)
# Output: [1]  8 NA Inf -Inf

Q: Given vector a, write code to count and replace NAs.

A: Assuming a <- c(10, 15, NA, 20).

  • Count NAs: sum(is.na(a)) → 1.
  • Replace NAs (e.g., with 0): a[is.na(a)] <- 0.

Q: Explain the difference between a[a > 12] and a[which(a > 12)].

A: Both select elements greater than 12 (15, 20). However:

  • a[a > 12][1] 15 NA (Uses logical indexing; preserves the position of NA in the original vector as NA).
  • a[which(
... Continue reading "R Programming Fundamentals, SQL, and Advanced Clustering Methods" »

Intervalos de confianza y pruebas estadísticas para muestras

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Problema 1: Intervalo para la media (σ conocido)

PROBLEM 1

Nos dan normal distribution.

Desviación estándar σ = 24

Muestra aleatoria simple de 8 — ignorar.

Luego los datos:

185, 180, 167, 162, 176, 170, 181, 192

La pregunta es: confidence interval for the population mean, with a confidence level of...

Paso 1: srs1 = c(185, 180, 162, etc...)

Pregunta (A) — 90%

Ejecutar:

z.test(x = srs1, sigma = 24, conf.level = 0.9)

Pregunta (B) — 95%

Ejecutar:

z.test(x = srs1, sigma = 24, conf.level = 0.95)

Pregunta (C) — 99%

Ejecutar:

z.test(x = srs1, sigma = 24, conf.level = 0.99)

Problema 2: Intervalo para media, varianza y desviación

PROBLEM 2

Te dan números: 45297, 51012, 41764, 41799, 42408, 28543

Normal distribution.

Nivel de significancia = 2% y necesito el

... Continue reading "Intervalos de confianza y pruebas estadísticas para muestras" »

Understanding Financial Formulas and Calculations

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Tutorial 1

If you get a positive value times a number,

You need to shift the decimal to the right as many times as the number specified.

If negative, move it to the right.

Simple interest formula = S = FV = P(1 + iK)

Compound interest formula = Sk = P(1 + i)^k

Sn = P(1 + I/T)^n
where I is interest
T is frequency of compounding per year
K is the number of years
N is the total number of periods - K T or TK

Depreciation Formula = Vo or P = Initial value,
Vk = P(1 - d)^k

Tutorial 2

1. 5 years 1 + r = (FV/PV)^(1/5)
(i) r = 10.38%
(ii) r = 10.47%
(iii) r = 10.51%
(iv) r = 10.52%
(v) r = 10.52%
2. 1 + r = (1 + 0.06/12)^8 ∙ (1 + 0.072/12)^4
1 + r = (1.005)^8 ∙ (1.006)^4
1 + r = (1.0407) ∙ (1.0242) = 1.06591
r = 6.59%

For an initial outlay of $1000, the net return is

... Continue reading "Understanding Financial Formulas and Calculations" »

Optimal Estimators, Dice Posterior & Statistical Problems

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Combine Independent Unbiased Estimators

Let d1 and d2 be independent unbiased estimators of θ with variances σ12 and σ22, respectively:

  • E[di] = θ for i = 1,2.
  • Var(di) = σi2.

Any estimator of the form d = λ d1 + (1 - λ) d2 is also unbiased for any constant λ.

The variance (mean square error for an unbiased estimator) is
Var(d) = λ2σ12 + (1 - λ)2σ22.

To minimize Var(d) with respect to λ, differentiate and set to zero:

d/dλ Var(d) = 2λσ12 - 2(1 - λ)σ22 = 0.

Solving gives the optimal weight

λ* = σ22 / (σ12 + σ22).


Question 1: Posterior PMF for a Third Dice Roll

Assume there are five dice with sides {4, 6, 8, 12, 20}. One of these five dice is selected uniformly at random (probability 1/5) and rolled twice. The two observed results are... Continue reading "Optimal Estimators, Dice Posterior & Statistical Problems" »