IGNOU MCO-22 December 2025 Previous Year Question Paper

Q1.Discuss the application of quantitative techniques in various functional areas of management. 20 [ 2 ]

Q2.What do you understand by the term ‘correlation’ ? Explain how the study of correlation helps in forecasting demand of a product.

Q3.(a) What are Ogives ? Discuss the method of constructing ogives with the help of an example. 10

(b) What is the practical utility of the central limit theorem in Applied Statistics ?

Q4.(a) Calculate the second coefficient of skewness (SK 2) using median for the following data : 12

(b) If the coefficient of skewness of a distribution is 0.32, the standard deviation is 6.5 and the mean is 29.6 , then find the mode of distribution. 8 [ 3 ]

Q5.What is the major difference between probability and non -probability sampling ?

Q6.List the various reasons that make sampling so attract ive in drawing conclusions about the population.

Q7.What is Chi -square distribution ? How would you use it in testing the goodness of fit and testing independence of categorized data ?

Q8.Why is forecasting so important in business ? Iden tify the applications of forecasting for the following : 5+5+5+5

(a) Long-term decisions

(b) Medium-term decisions

(c) Short-term decisions [ 4 ]

Q9.Write short notes on any four of the following : 4×5=20

(a) Geometric Mean

(b) Modal Class

(c) Rank Correlation

(d) Measure of Skewness

(e) Decision Environment in Managerial Application [ 5 ] MCO–22 okf.kT; esa LukrdksÙkj mikf/k (,e- dkWe-) l=kar ijh{kk fnlEcj] 2025 ,e-lh-vks-&22 % ek=kRed fo'ys"k.k vkSj izca/kdh; vuqiz;ksx le; % 3 ?k.Vs vf/kdre vad % 100 uksV % fdUgha ik¡p iz'uksa ds mÙkj nhft,A lHkh iz'uksa ds vad leku gSaA 1- çca/ku ds fofHkUu dk;kZRed {ks=ksa esa ek=kRed rduhdksa ds vuqç;ksx ij ppkZ dhft,A 20 [ 6 ] MCO–22 2- ^lglaca/k* 'kCn ls vki D;k le>rs gSa \ lglaca/k dk v/;;u fdlh mRikn dh ek¡ x dk iwokZuqeku yxkus esa dSls enn djrk gS \ O;k[;k dhft,A 5$15 3-

(d) rksj.k (Ogives) D;k gSa \ mnkgj.k dh lgk;rk ls rksj.k cukus dh fof/k ij ppkZ dhft,A 10 ([k) vuqç;qä lkaf[;dh esa dsUæh; lhek çes; dh O;kogkfjd mi;ksfxrk D;k gS \ 10 4-

(d) fuEufyf[kr MsVk ds fy, f o"kerk ds f}rh; xq.kkad (SK2) dh x.kuk dhft, % 12 ([k) ;fn fdlh forj.k dk fo"kerk xq.kkad 0.32 gS] ekud fopyu 6.5 gS vkSj ek/; 29.6 gS] rks forj.k dk cgqyd Kkr dhft,A 8 [ 7 ] MCO–22 5- laHkkO;rk vkSj xSj&laHkkO;rk uewus ds chp D;k çeq[k varj gS \ mu fofHkUu dkj.kksa dh lwph cukb, tks tula[;k ds ckjs esa fu"d"kZ fudkyus esa uewukdj.k dks bruk vkd"kZd cukrs gSaA 10$10 6- dkbZ&oxZ (Chi-square) forj.k D;k gS \ vki bldk mi;ksx fQV dh vPNkbZ dk ijh{k.k djus vkSj oxhZ Ïr MsVk dh Lora=rk dk ijh{k.k djus esa dSls djsaxs \ 5$15 7- O;olk; esa iwokZuqeku bruk egRoiw.kZ D;ksa gS \ fuEufyf[kr ds fy, iwokZuqeku ds vuqç;ksxksa dh igpku dhft, % 5$5$5$5

(d) nh?kZdkfyd fu.kZ; ([k) e/;e vof/k dk fu.kZ; (x) vYidkfyd fu.kZ; [ 8 ] MCO–22 8- fuEufyf[kr esa ls fdU gha pkj ij laf{kIr fVIif.k;k¡ fyf[k, % 4”5=20

(d) xq.kksÙkj ek/; ([k) cgqyd oxZ (x) jSad lglaca/k (?k) fo"kerk ds eki (³) çca/kdh; vuqç;ksx esa fu.kZ; okrkoj.k