This document contains solved assignments and study material related to Statistics and Probability. It covers topics such as mean, standard deviation, probability, linear regression and more. The content includes answers to questions related to days lost, defective components, normal distribution, and linear regression.
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Running head: STATISTICS AND PROBABILITY Statistics and Probability Name of the Student: Name of the University: Author’s note:
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1STATISTICS AND PROBABILITY Answer no 1. Days lost Mid-value (xi) Frequency (fi)(xi*fi)[(xi-μ)2]fi*[(xi-μ)2] 1 - 3'281634.2225273.78 4 - 6'57358.122556.8575 7 - 9'810800.02250.225 10 - 12'119999.922589.3025 13 - 15'1468437.8225226.935 Total4031490.1125647.1 Arithmetic mean = ∑ i=1 5 (xi∗fi) ∑ i=1 5 fi Standard deviation = ∑ i=1 5 [fi∗(xi−μ)]2 ∑ i=1 5 fi The arithmetic mean of the number of days lost is 7.85 days. The standard deviation of the number of days lost is 11.376 days.
2STATISTICS AND PROBABILITY Answer no 2. Part a Part b Part i Probability that the component was supplied by A = 60% = 0.60 Probability that the component is defective = 2% = 0.02 Hence the probability that a randomly selected component is supplied by A and is defective = 0.60 * 0.02 = 0.012 = 1.2% Part ii Probability that the component was supplied by B = 40% = 0.40 Probability that the component supplied by B is defective = 1% = 0.01 Hence the probability that a randomly selected component is supplied by B and is defective = 0.40 * 0.01 = 0.004 = 0.4% Hence the probability that a component is defective = 0.012 + 0.04 = 0.016 = 1.6% Part iii The probability that a component is supplied by A and is defective = 0.012 Electrical Component Company A (0.60) Defective (0.012) Correct (0.588) Company B (0.40) Defective (0.004) Correct (0.396)
3STATISTICS AND PROBABILITY Probability that a component is defective = 0.016 Hence, the probability that a component is defective and is supplied by Company A¿0.012 0.016 = 75.0 or 75% Answer no 3. The mean length of metal strips produced by a machine is normally distributed. The mean length is 150 cm and the standard deviation is 10 cm. Part a The probability that the length of a randomly selected strip is shorter than 165 cm. = P (X<165) =P[(X−μ) σ<(165−150) 10]= P[(X−μ) σ<1.5]= P[Z<1.5] = (1-0.06681) = 0.93319 Part b The probability that the length of a randomly selected strip is longer than 170 cm. = P (X>170) =P[(X−μ) σ>(170−150) 10]= P[(X−μ) σ>2]= P[Z>2] = 0.02275 Part c The probability that the length of a randomly selected strip is with the range of 145 cm. to 155 cm. = P (145<X<155) =P[(145−150) 10<(X−μ) σ<(155−150) 10]= P[−0.5<(X−μ) σ<0.5]= P [- 0.5<Z<0.5] = P[mod(Z)<0.5] = 0.3829. Answer no 4. Diameters (D mm)Torque (T Nm) 65.5 107 149.5 1812.5 2113.5
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4STATISTICS AND PROBABILITY 2516.5 Part a Figure1: Scatter plot of Torque vs. Diameter Part b The method of least squares for determining the linear regression equation that when dependent variable is Torque (Nm) and independent variable is Diameter (mm) is applied here. Figure 4: Residual plot of linear regression equation of Torque vs Diameter
5STATISTICS AND PROBABILITY Part c The linear regression model assuming Torque (Nm) as a dependent and Diameter (mm) as a independent variable is calculated as- Torque = (1.544786096) +0.587566845 * Diameters The value of torque when diameter is 16 mm, is found out to be 10.95 Nm. Part d The correlation coefficient ( r ) is calculated in this linear regression model as0.995405519. It refers a very strong link between dependent and independent variable. The value of multiple R2is found out to be 0.990832147. Therefore, the linear association between dependent and independent variable is very strong. Diameter can explain more than 99% variability of the Torque. The value of slope (0.587566845) is positive. Therefore, the with increment of diameter, Torque increases. With decrement of the diameter, Torque decreases. Per unit change of diameter, the torque changes by 0.587566845 units.