850 740 900 1070 930 850 950 980 980 880 1000 980 930 650 760 810...
850 740 900 1070 930 850 950 980 980 880 1000 980 930 650 760 810 1000 1000 1000 960 960 960 940 960 940 880 800 850 880 900 840 830 790 810 880 830 800 770 760 800 880 880 880 860 720 720 620 860 970 950 880 910 880 870 840 840 850 840 840 840 890 810 810 820 800 770 760 740 750 760 910 920 890 860 880 720 840 850 850 780 890 840 780 810 760 810 790 810 820 850 870 870 810 740 810 940 950 800 810 870. a) Find the sample skewness of Michelson data of the speed of light. b) Find the excess kurtosis of Michelson data of the speed of light
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Forecasting method
Use the moving average method to forecast period 105.Use the exponential smoothing method to forecast period 105.Use the time-series decomposition method to forecast period 105.Comparing the three methods, which one fits this situation best?The larger the parameter (n) is set, the more historical data are taken into account by the moving average.You can choose different parameter (n) to extrapolate to compare the prediction effect.In general, the parameter (n) should not be taken too large.Moving average.153025703560453055106560761085609580106101165012700136701470015760167301776018820197802090021840227702382024800257602676027770287902976030740317203267033690344703567036690376203865039610406204164042590436104460045630466004763048640496105059051610526305366054640558105679057820586505971060700616706269063730647306576066790678106887069890708707189072880739307498075900768607789078880798708084081860829108387084860858408654087780887508978090760917109273093750947509571096750977209877099740100750101760102780103800104850105Exponential smoothingsame153025703560453055106560761085609580106101165012700136701470015760167301776018820197802090021840227702382024800257602676027770287902976030740317203267033690344703567036690376203865039610406204164042590436104460045630466004763048640496105059051610526305366054640558105679057820586505971060700616706269063730647306576066790678106887069890708707189072880739307498075900768607789078880798708084081860829108387084860858408654087780887508978090760917109273093750947509571096750977209877099740100750101760102780103800104850105Time-series decomposition153025703560453055106560761085609580106101165012700136701470015760167301776018820197802090021840227702382024800257602676027770287902976030740317203267033690344703567036690376203865039610406204164042590436104460045630466004763048640496105059051610526305366054640558105679057820586505971060700616706269063730647306576066790678106887069890708707189072880739307498075900768607789078880798708084081860829108387084860858408654087780887508978090760917109273093750947509571096750977209877099740100750101760102780103800104850105
710 720 730 740 750 760 770780796) 305 816 826 830 840 850 860 870 880...
710 720 730 740 750 760 770780796) 305 816 826 830 840 850 860 870 880 890 900 910 920 930 QUESTION 2 The following information is for Bright Eyes Auto Supplies: Bright Eyes Auto Supplies Balance Sheet December 31, 2015 $ 130,000 50,000 150.000 330,000 740.000 Cash $ 40,000 Accounts Payable Prepaid Insurance 80,000 Salaries and Wages Payable Accounts Receivable 100,000 Mortgage Payable Inventory 140,000 Total Liabilities Land Held for Investment 180,000 Land 250,000 Buildings $200,000 Common Sock $400,000...
710 720 730 740 750 760 770 780 790 805 810 820) 836) 3465 850 866)...
710 720 730 740 750 760 770 780 790 805 810 820) 836) 3465 850 866) 370 380 390 904 QUESTION 4 The following information is for Bright Eyes Auto Supplies: Bright Eyes Auto Supplies Balance Sheet December 31, 2015 $ 130,000 50,000 150,000 330,000 Cash $ 40,000 Accounts Payable Prepaid Insurance 80,000 Salaries and Wages Payable Accounts Receivable 100,000 Mortgage Payable Inventory 140,000 Total Liabilities Land Held for Investment 180,000 Land 250,000 Buildings $200,000 Common Sock $400,000 Less Accumulated...
I need help with number 8 and 9. Thanks in advance. Questions 6-10 are related to...
I
need help with number 8 and 9. Thanks in advance.
Questions 6-10 are related to the following As a statistics course assignment you are to build a 95% confidence interval for the mean monthly rental for two-bedroom apartments in Marion County. A random sample of 80 apartments yields the following data. 670 1260 640 930 400 1440 1080 1230 960 870 970 740 1030 930 1420 1120 1270 940 1210 730 1040 1340 1100 1290 1040 700 1310 630...
Fit one-way ANOVA. Find the null and alternative hypotheses for the F-test and the corresponding p-values. Compare means using Bonferroni and Tukey confidence intervals. Amount (milligrams) Vol...
Fit one-way ANOVA. Find the null and alternative hypotheses for the F-test and the corresponding p-values. Compare means using Bonferroni and Tukey confidence intervals. Amount (milligrams) Volume (milliliters) 0 720 0 785 0 810 0 620 0 860 0 810 0 920 0 745 0 885 0 820 1 860 1 1055 1 960 1 805 1 915 1 845 1 950 1 750 1 1040 1 920 2 970 2 920 2 1020 2 830 2 650 2 1055...
To conduct a one-sided hypothesis test of the claim that houses located on corner lots (corner...
To conduct a one-sided hypothesis test of the claim that houses located on corner lots (corner lot houses) have higher average selling prices than those located on non-corner lots, which of the following alternative hypotheses should be used? The average selling price of a corner-lot house is higher than it is commonly believed to be. The average selling price of a corner-lot house is higher than the average selling price of all houses. The average selling price of a corner-lot...
Problem 10-31 (Algo) Large-scale integrated (LSI) circuit chips are made in one department of an electronics...
Problem 10-31 (Algo) Large-scale integrated (LSI) circuit chips are made in one department of an electronics firm. These chips are incorporated into analog devices that are then encased in epoxy. The yield is not particularly good for LSI manufacture, so the AQL specified by that department is 20% while the LTPD acceptable by the assembly department is 52%. Assume the company is willing to accept a consumer's risk of 10 percent and a producer's risk of 5 percent. Use Exhibit...
Tendai is a welder who started his own business making security gates, fences and other related steel product . Unit of...
Tendai is a welder who started his own business making security gates, fences and other related steel product . Unit of capital employed Unit of Capital employed Output quantity (Q) 10 340 560 800 1 000 1 160 1 380 1 650 1 950 1 980 2 200 9 400 620 840 1 030 1 210 1 440 1 770 2 000 2 050 2 100 8 380 540 810 980 1 090 1 400 1 700 1 980 2...
Tendai is a welder who started his own business making security gates, fences and other related...
Tendai is a welder who started his own business making security gates, fences and other related steel product . Unit of capital employed Unit of Capital employed Output quantity (Q) 10 340 560 800 1 000 1 160 1 380 1 650 1 950 1 980 2 200 9 400 620 840 1 030 1 210 1 440 1 770 2 000 2 050 2 100 8 380 540 810 980 1 090 1 400 1 700 1 980...
In C++ Programming: Using a single for loop, output the even numbers between 2 and 1004...
In C++ Programming: Using a single for loop, output the even numbers between 2 and 1004 (inclusive) that iterates (loops) exactly 502 times. The outputted numbers be aligned in a table with 10 numbers per row. Each column in the table should be 5 characters wide. Do not nest a loop inside of another loop. Hint: First create and test the code that output the numbers all on one line (the command line will automatically wrap the output to new...
Use the moving average method to forecast period 105.Use the exponential smoothing method to forecast period 105.Use the time-series decomposition method to forecast period 105.Comparing the three methods, which one fits this situation best?The larger the parameter (n) is set, the more historical data are taken into account by the moving average.You can choose different parameter (n) to extrapolate to compare the prediction effect.In general, the parameter (n) should not be taken too large.Moving average.153025703560453055106560761085609580106101165012700136701470015760167301776018820197802090021840227702382024800257602676027770287902976030740317203267033690344703567036690376203865039610406204164042590436104460045630466004763048640496105059051610526305366054640558105679057820586505971060700616706269063730647306576066790678106887069890708707189072880739307498075900768607789078880798708084081860829108387084860858408654087780887508978090760917109273093750947509571096750977209877099740100750101760102780103800104850105Exponential smoothingsame153025703560453055106560761085609580106101165012700136701470015760167301776018820197802090021840227702382024800257602676027770287902976030740317203267033690344703567036690376203865039610406204164042590436104460045630466004763048640496105059051610526305366054640558105679057820586505971060700616706269063730647306576066790678106887069890708707189072880739307498075900768607789078880798708084081860829108387084860858408654087780887508978090760917109273093750947509571096750977209877099740100750101760102780103800104850105Time-series decomposition153025703560453055106560761085609580106101165012700136701470015760167301776018820197802090021840227702382024800257602676027770287902976030740317203267033690344703567036690376203865039610406204164042590436104460045630466004763048640496105059051610526305366054640558105679057820586505971060700616706269063730647306576066790678106887069890708707189072880739307498075900768607789078880798708084081860829108387084860858408654087780887508978090760917109273093750947509571096750977209877099740100750101760102780103800104850105
710 720 730 740 750 760 770780796) 305 816 826 830 840 850 860 870 880 890 900 910 920 930 QUESTION 2 The following information is for Bright Eyes Auto Supplies: Bright Eyes Auto Supplies Balance Sheet December 31, 2015 $ 130,000 50,000 150.000 330,000 740.000 Cash $ 40,000 Accounts Payable Prepaid Insurance 80,000 Salaries and Wages Payable Accounts Receivable 100,000 Mortgage Payable Inventory 140,000 Total Liabilities Land Held for Investment 180,000 Land 250,000 Buildings $200,000 Common Sock $400,000...
710 720 730 740 750 760 770 780 790 805 810 820) 836) 3465 850 866) 370 380 390 904 QUESTION 4 The following information is for Bright Eyes Auto Supplies: Bright Eyes Auto Supplies Balance Sheet December 31, 2015 $ 130,000 50,000 150,000 330,000 Cash $ 40,000 Accounts Payable Prepaid Insurance 80,000 Salaries and Wages Payable Accounts Receivable 100,000 Mortgage Payable Inventory 140,000 Total Liabilities Land Held for Investment 180,000 Land 250,000 Buildings $200,000 Common Sock $400,000 Less Accumulated...
I
need help with number 8 and 9. Thanks in advance.
Questions 6-10 are related to the following As a statistics course assignment you are to build a 95% confidence interval for the mean monthly rental for two-bedroom apartments in Marion County. A random sample of 80 apartments yields the following data. 670 1260 640 930 400 1440 1080 1230 960 870 970 740 1030 930 1420 1120 1270 940 1210 730 1040 1340 1100 1290 1040 700 1310 630...
To conduct a one-sided hypothesis test of the claim that houses located on corner lots (corner lot houses) have higher average selling prices than those located on non-corner lots, which of the following alternative hypotheses should be used? The average selling price of a corner-lot house is higher than it is commonly believed to be. The average selling price of a corner-lot house is higher than the average selling price of all houses. The average selling price of a corner-lot...
Problem 10-31 (Algo) Large-scale integrated (LSI) circuit chips are made in one department of an electronics firm. These chips are incorporated into analog devices that are then encased in epoxy. The yield is not particularly good for LSI manufacture, so the AQL specified by that department is 20% while the LTPD acceptable by the assembly department is 52%. Assume the company is willing to accept a consumer's risk of 10 percent and a producer's risk of 5 percent. Use Exhibit...
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